Category Archives: learning system (LS)

ACI – TWO DIFFERENT READINGS

eJournal: uffmm.org
ISSN 2567-6458, 11.-12.May 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de
Change: May-17, 2019 (Some Corrections, ACI associations)
Change: May-20, 2019 (Reframing ACI with AAI)
CONTEXT

This text is part of the larger text dealing with the Actor-Actor Interaction (AAI)  paradigm.

HCI – HMI – AAI ==> ACI ?

Who has followed the discussion in this blog remembers several different phases in the conceptual frameworks used here.

The first paradigm called Human-Computer Interface (HCI) has been only mentioned by historical reasons.  The next phase Human-Machine Interaction (HMI) was the main paradigm in the beginning of my lecturing in 2005. Later, somewhere 2011/2012, I switched to the paradigm Actor-Actor Interaction (AAI) because I tried to generalize over  the different participating machines, robots, smart interfaces, humans as well as animals. This worked quite nice and some time I thought that this is now the final formula. But reality is often different compared to  our thinking. Many occasions showed up where the generalization beyond the human actor seemed to hide the real processes which are going on, especially I got the impression that very important factors rooted in the special human actor became invisible although they are playing decisive role in many  processes. Another punch against the AAI view came from application scenarios during the last year when I started to deal with whole cities as actors. At the end  I got the feeling that the more specialized expressions like   Actor-Cognition Interaction (ACI) or  Augmented Collective Intelligence (ACI) can indeed help  to stress certain  special properties  better than the more abstract AAI acronym, but using structures like ACI  within general theories and within complex computing environments it became clear that the more abstract acronym AAI is in the end more versatile and simplifies the general structures. ACI became a special sub-case

HISTORY

To understand this oscillation between AAI and  ACI one has to look back into the history of Human Computer/ Machine Interaction, but not only until the end of the World War II, but into the more extended evolutionary history of mankind on this planet.

It is a widespread opinion under the researchers that the development of tools to help mastering material processes was one of the outstanding events which changed the path of  the evolution a lot.  A next step was the development of tools to support human cognition like scripture, numbers, mathematics, books, libraries etc. In this last case of cognitive tools the material of the cognitive  tools was not the primary subject the processes but the cognitive contents, structures, even processes encoded by the material structures of the tools.

Only slowly mankind understood how the cognitive abilities and capabilities are rooted in the body, in the brain, and that the brain represents a rather complex biological machinery which enables a huge amount of cognitive functions, often interacting with each other;  these cognitive functions show in the light of observable behavior clear limits with regard to the amount of features which can be processed in some time interval, with regard to precision, with regard to working interconnections, and more. And therefore it has been understood that the different kinds of cognitive tools are very important to support human thinking and to enforce it in some ways.

Only in the 20th century mankind was able to built a cognitive tool called computer which could show   capabilities which resembled some human cognitive capabilities and which even surpassed human capabilities in some limited areas. Since then these machines have developed a lot (not by themselves but by the thinking and the engineering of humans!) and meanwhile the number and variety of capabilities where the computer seems to resemble a human person or surpasses human capabilities have extend in a way that it has become a common slang to talk about intelligent machines or smart devices.

While the original intention for the development of computers was to improve the cognitive tools with the intend  to support human beings one can  get today  the impression as if the computer has turned into a goal on its own: the intelligent and then — as supposed — the super-intelligent computer appears now as the primary goal and mankind appears as some old relic which has to be surpassed soon.

As will be shown later in this text this vision of the computer surpassing mankind has some assumptions which are

What seems possible and what seems to be a promising roadmap into the future is a continuous step-wise enhancement of the biological structure of mankind which absorbs the modern computing technology by new cognitive interfaces which in turn presuppose new types of physical interfaces.

To give a precise definition of these new upcoming structures and functions is not yet possible, but to identify the actual driving factors as well as the exciting combinations of factors seems possible.

COGNITION EMBEDDED IN MATTER
Actor-Cognition Interaction (ACI): A simple outline of the whole paradigm
Cognition within the Actor-Actor Interaction (AAI)  paradigm: A simple outline of the whole paradigm

The main idea is the shift of the focus away from the physical grounding of the interaction between actors looking instead more to the cognitive contents and processes, which shall be mediated  by the physical conditions. Clearly the analysis of the physical conditions as well as the optimal design of these physical conditions is still a challenge and a task, but without a clear knowledge manifested in a clear model about the intended cognitive contents and processes one has not enough knowledge for the design of the physical layout.

SOLVING A PROBLEM

Thus the starting point of an engineering process is a group of people (the stakeholders (SH)) which identify some problem (P) in their environment and which have some minimal idea of a possible solution (S) for this problem. This can be commented by some non-functional requirements (NFRs) articulating some more general properties which shall hold through the whole solution (e.g. ‘being save’, ‘being barrier-free’, ‘being real-time’ etc.). If the description of the problem with a first intended solution including the NFRs contains at least one task (T) to be solved, minimal intended users (U) (here called executive actors (eA)), minimal intended assistive actors (aA) to assist the user in doing the task, as well as a description of the environment of the task to do, then the minimal ACI-Check can be passed and the ACI analysis process can be started.

COGNITION AND AUGMENTED COLLECTIVE INTELLIGENCE

If we talk about cognition then we think usually about cognitive processes in an individual person.  But in the real world there is no cognition without an ongoing exchange between different individuals by communicative acts. Furthermore it has to be taken into account that the cognition of an individual person is in itself partitioned into two unequal parts: the unconscious part which covers about 99% of all the processes in the body and in the brain and about 1% which covers the conscious part. That an individual person can think somehow something this person has to trigger his own unconsciousness by stimuli to respond with some messages from his before unknown knowledge. Thus even an individual person alone has to organize a communication with his own unconsciousness to be able to have some conscious knowledge about its own unconscious knowledge. And because no individual person has at a certain point of time a clear knowledge of his unconscious knowledge  the person even does not really know what to look for — if there is no event, not perception, no question and the like which triggers the person to interact with its unconscious knowledge (and experience) to get some messages from this unconscious machinery, which — as it seems — is working all the time.

On account of this   logic of the individual internal communication with the individual cognition  an external communication with the world and the manifested cognition of other persons appears as a possible enrichment in the   interactions with the distributed knowledge in the different persons. While in the following approach it is assumed to represent the different knowledge responses in a common symbolic representation viewable (and hearable)  from all participating persons it is growing up a possible picture of something which is generally more rich, having more facets than a picture generated by an individual person alone. Furthermore can such a procedure help all participants to synchronize their different knowledge fragments in a bigger picture and use it further on as their own picture, which in turn can trigger even more aspects out of the distributed unconscious knowledge.

If one organizes this collective triggering of distributed unconscious knowledge within a communication process not only by static symbolic models but beyond this with dynamic rules for changes, which can be interactively simulated or even played with defined states of interest then the effect of expanding the explicit and shared knowledge will be boosted even more.

From this background it makes some sense to turn the wording Actor-Cognition Interaction into the wording Augmented Collective Intelligence where Intelligence is the component of dynamic cognition in a system — here a human person –, Collective means that different individual person are sharing their unconscious knowledge by communicative interactions, and Augmented can be interpreted that one enhances, extends this sharing of knowledge by using new tools of modeling, simulation and gaming, which expands and intensifies the individual learning as well as the commonly shared opinions. For nearly all problems today this appears to be  absolutely necessary.

ACI ANALYSIS PROCESS

Here it will be assumed that there exists a group of ACI experts which can supervise  other actors (stakeholders, domain experts, …) in a process to analyze the problem P with the explicit goal of finding a satisfying solution (S+).

For the whole ACI analysis process an appropriate ACI software should be available to support the ACI experts as well as all the other domain experts.

In this ACI analysis process one can distinguish two main phases: (1) Construct an actor story (AS) which describes all intended states and intended changes within the actor story. (2) Make several tests of the actor story to exploit their explanatory power.

ACTOR STORY (AS)

The actor story describes all possible states (S) of the tasks (T) to be realized to reach intended goal states (S+). A mapping from one state to a follow-up state will be described by a change rule (X). Thus having start state (S0) and appropriate change rules one can construct the follow-up states from the actual state (S*)  with the aid of the change rules. Formally this computation of the follow-up state (S’) will be computed by a simulator function (σ), written as: σ: S* x X  —> S.

SEVERAL TESTS

With the aid of an explicit actor story (AS) one can define the non-functional requirements (NFRs) in a way that it will become decidable whether  a NFR is valid with regard to an actor story or not. In this case this test of being valid can be done as an automated verification process (AVP). Part of this test paradigm is the so-called oracle function (OF) where one can pose a question to the system and the system will answer the question with regard to all theoretically possible states without the necessity to run a (passive) simulation.

If the size of the group is large and it is important that all members of the group have a sufficient similar knowledge about the problem(s) in question (as it is the usual case in a city with different kinds of citizens) then is can be very helpful to enable interactive simulations or even games, which allow a more direct experience of the possible states and changes. Furthermore, because the participants can act according to their individual reflections and goals the process becomes highly uncertain and nearly unpredictable. Especially for these highly unpredictable processes can interactive simulations (and games) help to improve a common understanding of the involved factors and their effects. The difference between a normal interactive simulation and a game is given in the fact that a game has explicit win-states whereas the interactive simulations doesn’t. Explicit win-states can improve learning a lot.

The other interesting question is whether an actor story AS with a certain idea for an assistive actor (aA) is usable for the executive actors. This requires explicit measurements of the usability which in turn requires a clear norm of reference with which the behavior of an executive actor (eA) during a process can be compared. Usually is the actor Story as such the norm of reference with which the observable behavior of the executing actors will be compared. Thus for the measurement one needs real executive actors which represent the intended executive actors and one needs a physical realization of the intended assistive actors called mock-up. A mock-up is not yet  the final implementation of the intended assistive actor but a physical entity which can show all important physical properties of the intended assistive actor in a way which allows a real test run. While in the past it has been assumed to be sufficient to test a test person only once it is here assumed that a test person has to be tested at least three times. This follows from the assumption that every executive (biological) actor is inherently a learning system. This implies that the test person will behave differently in different tests. The degree of changes can be a hint of the easiness and the learnability of the assistive actor.

COLLECTIVE MEMORY

If an appropriate ACI software is available then one can consider an actor story as a simple theory (ST) embracing a model (M) and a collection of rules (R) — ST(x) iff x = <M,R> –which can be used as a kind of a     building block which in turn can be combined with other such building blocks resulting in a complex network of simple theories. If these simple theories are stored in a  public available data base (like a library of theories) then one can built up in time a large knowledge base on their own.

 

 

ENGINEERING AND SOCIETY: The Role of Preferences

eJournal: uffmm.org,
ISSN 2567-6458, 4.May 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

FINAL HYPOTHESIS

This suggests that a symbiosis between creative humans and computing algorithms is an attractive pairing. For this we have to re-invent our official  learning processes in schools and universities to train the next generation of humans in a more inspired and creative usage of algorithms in a game-like learning processes.

CONTEXT

The overall context is given by the description of the Actor-Actor Interaction (AAI) paradigm as a whole.  In this text the special relationship between engineering and the surrounding society is in the focus. And within this very broad and rich relationship the main interest lies in the ethical dimension here understood as those preferences of a society which are more supported than others. It is assumed that such preferences manifesting themselves  in real actions within a space of many other options are pointing to hidden values which guide the decisions of the members of a society. Thus values are hypothetical constructs based on observable actions within a cognitively assumed space of possible alternatives. These cognitively represented possibilities are usually only given in a mixture of explicitly stated symbolic statements and different unconscious factors which are influencing the decisions which are causing the observable actions.

These assumptions represent  until today not a common opinion and are not condensed in some theoretical text. Nevertheless I am using these assumptions here because they help to shed some light on the rather complex process of finding a real solution to a stated problem which is rooted in the cognitive space of the participants of the engineering process. To work with these assumptions in concrete development processes can support a further clarification of all these concepts.

ENGINEERING AND SOCIETY

DUAL: REAL AND COGNITIVE

The relationship between an engineering process and the preferences of a society
The relationship between an engineering process and the preferences of a society

As assumed in the AAI paradigm the engineering process is that process which connects the  event of  stating a problem combined with a first vision of a solution with a final concrete working solution.

The main characteristic of such an engineering process is the dual character of a continuous interaction between the cognitive space of all participants of the process with real world objects, actions, and processes. The real world as such is a lose collection of real things, to some extend connected by regularities inherent in natural things, but the visions of possible states, possible different connections, possible new processes is bound to the cognitive space of biological actors, especially to humans as exemplars of the homo sapiens species.

Thus it is a major factor of training, learning, and education in general to see how the real world can be mapped into some cognitive structures, how the cognitive structures can be transformed by cognitive operations into new structures and how these new cognitive structures can be re-mapped into the real world of bodies.

Within the cognitive dimension exists nearly infinite sets of possible alternatives, which all indicate possible states of a world, whose feasibility is more or less convincing. Limited by time and resources it is usually not possible to explore all these cognitively tapped spaces whether and how they work, what are possible side effects etc.

PREFERENCES

Somehow by nature, somehow by past experience biological system — like the home sapiens — have developed   cultural procedures to induce preferences how one selects possible options, which one should be selected, under which circumstances and with even more constraints. In some situations these preferences can be helpful, in others they can  hide possibilities which afterwards can be  re-detected as being very valuable.

Thus every engineering process which starts  a transformation process from some cognitively given point of view to a new cognitively point of view with a following up translation into some real thing is sharing its cognitive space with possible preferences of  the cognitive space of the surrounding society.

It is an open case whether the engineers as the experts have an experimental, creative attitude to explore without dogmatic constraints the   possible cognitive spaces to find new solutions which can improve life or not. If one assumes that there exist no absolute preferences on account of the substantially limit knowledge of mankind at every point of time and inferring from this fact the necessity to extend an actual knowledge further to enable the mastering of an open unknown future then the engineers will try to explore seriously all possibilities without constraints to extend the power of engineering deeper into the heart of the known as well as unknown universe.

EXPLORING COGNITIVE POSSIBILITIES

At the start one has only a rough description of the problem and a rough vision of a wanted solution which gives some direction for the search of an optimal solution. This direction represents also a kind of a preference what is wanted as the outcome of the process.

On account of the inherent duality of human thinking and communication embracing the cognitive space as well as the realm of real things which both are connected by complex mappings realized by the brain which operates  nearly completely unconscious a long process of concrete real and cognitive actions is necessary to materialize cognitive realities within a  communication process. Main modes of materialization are the usage of symbolic languages, paintings (diagrams), physical models, algorithms for computation and simulations, and especially gaming (in several different modes).

As everybody can know  these communication processes are not simple, can be a source of  confusions, and the coordination of different brains with different cognitive spaces as well as different layouts of unconscious factors  is a difficult and very demanding endeavor.

The communication mode gaming is of a special interest here  because it is one of the oldest and most natural modes to learn but in the official education processes in schools and  universities (and in companies) it was until recently not part of the official curricula. But it is the only mode where one can exercise the dimensions of preferences explicitly in combination with an exploring process and — if one wants — with the explicit social dimension of having more than one brain involved.

In the last about 50 – 100 years the term project has gained more and more acceptance and indeed the organization of projects resembles a game but it is usually handled as a hierarchical, constraints-driven process where creativity and concurrent developing (= gaming) is not a main topic. Even if companies allow concurrent development teams these teams are cognitively separated and the implicit cognitive structures are black boxes which can not be evaluated as such.

In the presupposed AAI paradigm here the open creative space has a high priority to increase the chance for innovation. Innovation is the most valuable property in face of an unknown future!

While the open space for a real creativity has to be executed in all the mentioned modes of communication the final gaming mode is of special importance.  To enable a gaming process one has explicitly to define explicit win-lose states. This  objectifies values/ preferences hidden   in the cognitive space before. Such an  objectification makes things transparent, enables more rationality and allows the explicit testing of these defined win-lose states as feasible or not. Only tested hypothesis represent tested empirical knowledge. And because in a gaming mode whole groups or even all members of a social network can participate in a  learning process of the functioning and possible outcome of a presented solution everybody can be included.  This implies a common sharing of experience and knowledge which simplifies the communication and therefore the coordination of the different brains with their unconsciousness a lot.

TESTING AND EVALUATION

Testing a proposed solution is another expression for measuring the solution. Measuring is understood here as a basic comparison between the target to be measured (here the proposed solution) and the before agreed norm which shall be used as point of reference for the comparison.

But what can be a before agreed norm?

Some aspects can be mentioned here:

  1. First of all there is the proposed solution as such, which is here a proposal for a possible assistive actor in an assumed environment for some intended executive actors which has to fulfill some job (task).
  2. Part of this proposed solution are given constraints and non-functional requirements.
  3. Part of this proposed solution are some preferences as win-lose states which have to be reached.
  4. Another difficult to define factor are the executive actors if they are biological systems. Biological systems with their basic built in ability to act free, to be learning systems, and this associated with a not-definable large unconscious realm.

Given the explicit preferences constrained by many assumptions one can test only, whether the invited test persons understood as possible instances of the  intended executive actors are able to fulfill the defined task(s) in some predefined amount of time within an allowed threshold of making errors with an expected percentage of solved sub-tasks together with a sufficient subjective satisfaction with the whole process.

But because biological executive actors are learning systems they  will behave in different repeated  tests differently, they can furthermore change their motivations and   their interests, they can change their emotional commitment, and because of their   built-in basic freedom to act there can be no 100% probability that they will act at time t as they have acted all the time before.

Thus for all kinds of jobs where the process is more or less fixed, where nothing new  will happen, the participation of biological executive actors in such a process is questionable. It seems (hypothesis), that biological executing actors are better placed  in jobs where there is some minimal rate of curiosity, of innovation, and of creativity combined with learning.

If this hypothesis is empirically sound (as it seems), then all jobs where human persons are involved should have more the character of games then something else.

It is an interesting side note that the actual research in robotics under the label of developmental robotics is struck by the problem how one can make robots continuously learning following interesting preferences. Given a preference an algorithm can work — under certain circumstances — often better than a human person to find an optimal solution, but lacking such a preference the algorithm is lost. And actually there exists not the faintest idea how algorithms should acquire that kind of preferences which are interesting and important for an unknown future.

On the contrary, humans are known to be creative, innovative, detecting new preferences etc. but they have only limited capacities to explore these creative findings until some telling endpoint.

This suggests that a symbiosis between creative humans and computing algorithms is an attractive pairing. For this we have to re-invent our official  learning processes in schools and universities to train the next generation of humans in a more inspired and creative usage of algorithms in a game-like learning processes.

 

 

 

 

AAI THEORY V2 –A Philosophical Framework

eJournal: uffmm.org,
ISSN 2567-6458, 22.February 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

Last change: 23.February 2019 (continued the text)

Last change: 24.February 2019 (extended the text)

CONTEXT

In the overview of the AAI paradigm version 2 you can find this section  dealing with the philosophical perspective of the AAI paradigm. Enjoy reading (or not, then send a comment :-)).

THE DAILY LIFE PERSPECTIVE

The perspective of Philosophy is rooted in the everyday life perspective. With our body we occur in a space with other bodies and objects; different features, properties  are associated with the objects, different kinds of relations an changes from one state to another.

From the empirical sciences we have learned to see more details of the everyday life with regard to detailed structures of matter and biological life, with regard to the long history of the actual world, with regard to many interesting dynamics within the objects, within biological systems, as part of earth, the solar system and much more.

A certain aspect of the empirical view of the world is the fact, that some biological systems called ‘homo sapiens’, which emerged only some 300.000 years ago in Africa, show a special property usually called ‘consciousness’ combined with the ability to ‘communicate by symbolic languages’.

General setting of the homo sapiens species (simplified)
Figure 1: General setting of the homo sapiens species (simplified)

As we know today the consciousness is associated with the brain, which in turn is embedded in the body, which  is further embedded in an environment.

Thus those ‘things’ about which we are ‘conscious’ are not ‘directly’ the objects and events of the surrounding real world but the ‘constructions of the brain’ based on actual external and internal sensor inputs as well as already collected ‘knowledge’. To qualify the ‘conscious things’ as ‘different’ from the assumed ‘real things’ ‘outside there’ it is common to speak of these brain-generated virtual things either as ‘qualia’ or — more often — as ‘phenomena’ which are  different to the assumed possible real things somewhere ‘out there’.

PHILOSOPHY AS FIRST PERSON VIEW

‘Philosophy’ has many facets.  One enters the scene if we are taking the insight into the general virtual character of our primary knowledge to be the primary and irreducible perspective of knowledge.  Every other more special kind of knowledge is necessarily a subspace of this primary phenomenological knowledge.

There is already from the beginning a fundamental distinction possible in the realm of conscious phenomena (PH): there are phenomena which can be ‘generated’ by the consciousness ‘itself’  — mostly called ‘by will’ — and those which are occurring and disappearing without a direct influence of the consciousness, which are in a certain basic sense ‘given’ and ‘independent’,  which are appearing  and disappearing according to ‘their own’. It is common to call these independent phenomena ’empirical phenomena’ which represent a true subset of all phenomena: PH_emp  PH. Attention: These empirical phenomena’ are still ‘phenomena’, virtual entities generated by the brain inside the brain, not directly controllable ‘by will’.

There is a further basic distinction which differentiates the empirical phenomena into those PH_emp_bdy which are controlled by some processes in the body (being tired, being hungry, having pain, …) and those PH_emp_ext which are controlled by objects and events in the environment beyond the body (light, sounds, temperature, surfaces of objects, …). Both subsets of empirical phenomena are different: PH_emp_bdy PH_emp_ext = 0. Because phenomena usually are occurring  associated with typical other phenomena there are ‘clusters’/ ‘pattern’ of phenomena which ‘represent’ possible events or states.

Modern empirical science has ‘refined’ the concept of an empirical phenomenon by introducing  ‘standard objects’ which can be used to ‘compare’ some empirical phenomenon with such an empirical standard object. Thus even when the perception of two different observers possibly differs somehow with regard to a certain empirical phenomenon, the additional comparison with an ’empirical standard object’ which is the ‘same’ for both observers, enhances the quality, improves the precision of the perception of the empirical phenomena.

From these considerations we can derive the following informal definitions:

  1. Something is ‘empirical‘ if it is the ‘real counterpart’ of a phenomenon which can be observed by other persons in my environment too.
  2. Something is ‘standardized empirical‘ if it is empirical and can additionally be associated with a before introduced empirical standard object.
  3. Something is ‘weak empirical‘ if it is the ‘real counterpart’ of a phenomenon which can potentially be observed by other persons in my body as causally correlated with the phenomenon.
  4. Something is ‘cognitive‘ if it is the counterpart of a phenomenon which is not empirical in one of the meanings (1) – (3).

It is a common task within philosophy to analyze the space of the phenomena with regard to its structure as well as to its dynamics.  Until today there exists not yet a complete accepted theory for this subject. This indicates that this seems to be some ‘hard’ task to do.

BRIDGING THE GAP BETWEEN BRAINS

As one can see in figure 1 a brain in a body is completely disconnected from the brain in another body. There is a real, deep ‘gap’ which has to be overcome if the two brains want to ‘coordinate’ their ‘planned actions’.

Luckily the emergence of homo sapiens with the new extended property of ‘consciousness’ was accompanied by another exciting property, the ability to ‘talk’. This ability enabled the creation of symbolic languages which can help two disconnected brains to have some exchange.

But ‘language’ does not consist of sounds or a ‘sequence of sounds’ only; the special power of a language is the further property that sequences of sounds can be associated with ‘something else’ which serves as the ‘meaning’ of these sounds. Thus we can use sounds to ‘talk about’ other things like objects, events, properties etc.

The single brain ‘knows’ about the relationship between some sounds and ‘something else’ because the brain is able to ‘generate relations’ between brain-structures for sounds and brain-structures for something else. These relations are some real connections in the brain. Therefore sounds can be related to ‘something  else’ or certain objects, and events, objects etc.  can become related to certain sounds. But these ‘meaning relations’ can only ‘bridge the gap’ to another brain if both brains are using the same ‘mapping’, the same ‘encoding’. This is only possible if the two brains with their bodies share a real world situation RW_S where the perceptions of the both brains are associated with the same parts of the real world between both bodies. If this is the case the perceptions P(RW_S) can become somehow ‘synchronized’ by the shared part of the real world which in turn is transformed in the brain structures P(RW_S) —> B_S which represent in the brain the stimulating aspects of the real world.  These brain structures B_S can then be associated with some sound structures B_A written as a relation  MEANING(B_S, B_A). Such a relation  realizes an encoding which can be used for communication. Communication is using sound sequences exchanged between brains via the body and the air of an environment as ‘expressions’ which can be recognized as part of a learned encoding which enables the receiving brain to identify a possible meaning candidate.

DIFFERENT MODES TO EXPRESS MEANING

Following the evolution of communication one can distinguish four important modes of expressing meaning, which will be used in this AAI paradigm.

VISUAL ENCODING

A direct way to express the internal meaning structures of a brain is to use a ‘visual code’ which represents by some kinds of drawing the visual shapes of objects in the space, some attributes of  shapes, which are common for all people who can ‘see’. Thus a picture and then a sequence of pictures like a comic or a story board can communicate simple ideas of situations, participating objects, persons and animals, showing changes in the arrangement of the shapes in the space.

Pictorial expressions representing aspects of the visual and the auditory sens modes
Figure 2: Pictorial expressions representing aspects of the visual and the auditory sens modes

Even with a simple visual code one can generate many sequences of situations which all together can ‘tell a story’. The basic elements are a presupposed ‘space’ with possible ‘objects’ in this space with different positions, sizes, relations and properties. One can even enhance these visual shapes with written expressions of  a spoken language. The sequence of the pictures represents additionally some ‘timely order’. ‘Changes’ can be encoded by ‘differences’ between consecutive pictures.

FROM SPOKEN TO WRITTEN LANGUAGE EXPRESSIONS

Later in the evolution of language, much later, the homo sapiens has learned to translate the spoken language L_s in a written format L_w using signs for parts of words or even whole words.  The possible meaning of these written expressions were no longer directly ‘visible’. The meaning was now only available for those people who had learned how these written expressions are associated with intended meanings encoded in the head of all language participants. Thus only hearing or reading a language expression would tell the reader either ‘nothing’ or some ‘possible meanings’ or a ‘definite meaning’.

A written textual version in parallel to a pictorial version
Figure 3: A written textual version in parallel to a pictorial version

If one has only the written expressions then one has to ‘know’ with which ‘meaning in the brain’ the expressions have to be associated. And what is very special with the written expressions compared to the pictorial expressions is the fact that the elements of the pictorial expressions are always very ‘concrete’ visual objects while the written expressions are ‘general’ expressions allowing many different concrete interpretations. Thus the expression ‘person’ can be used to be associated with many thousands different concrete objects; the same holds for the expression ‘road’, ‘moving’, ‘before’ and so on. Thus the written expressions are like ‘manufacturing instructions’ to search for possible meanings and configure these meanings to a ‘reasonable’ complex matter. And because written expressions are in general rather ‘abstract’/ ‘general’ which allow numerous possible concrete realizations they are very ‘economic’ because they use minimal expressions to built many complex meanings. Nevertheless the daily experience with spoken and written expressions shows that they are continuously candidates for false interpretations.

FORMAL MATHEMATICAL WRITTEN EXPRESSIONS

Besides the written expressions of everyday languages one can observe later in the history of written languages the steady development of a specialized version called ‘formal languages’ L_f with many different domains of application. Here I am  focusing   on the formal written languages which are used in mathematics as well as some pictorial elements to ‘visualize’  the intended ‘meaning’ of these formal mathematical expressions.

Properties of an acyclic directed graph with nodes (vertices) and edges (directed edges = arrows)
Fig. 4: Properties of an acyclic directed graph with nodes (vertices) and edges (directed edges = arrows)

One prominent concept in mathematics is the concept of a ‘graph’. In  the basic version there are only some ‘nodes’ (also called vertices) and some ‘edges’ connecting the nodes.  Formally one can represent these edges as ‘pairs of nodes’. If N represents the set of nodes then N x N represents the set of all pairs of these nodes.

In a more specialized version the edges are ‘directed’ (like a ‘one way road’) and also can be ‘looped back’ to a node   occurring ‘earlier’ in the graph. If such back-looping arrows occur a graph is called a ‘cyclic graph’.

Directed cyclic graph extended to represent 'states of affairs'
Fig.5: Directed cyclic graph extended to represent ‘states of affairs’

If one wants to use such a graph to describe some ‘states of affairs’ with their possible ‘changes’ one can ‘interpret’ a ‘node’ as  a state of affairs and an arrow as a change which turns one state of affairs S in a new one S’ which is minimally different to the old one.

As a state of affairs I  understand here a ‘situation’ embedded in some ‘context’ presupposing some common ‘space’. The possible ‘changes’ represented by arrows presuppose some dimension of ‘time’. Thus if a node n’  is following a node n indicated by an arrow then the state of affairs represented by the node n’ is to interpret as following the state of affairs represented in the node n with regard to the presupposed time T ‘later’, or n < n’ with ‘<‘ as a symbol for a timely ordering relation.

Example of a state of affairs with a 2-dimensional space configured as a grid with a black and a white token
Fig.6: Example of a state of affairs with a 2-dimensional space configured as a grid with a black and a white token

The space can be any kind of a space. If one assumes as an example a 2-dimensional space configured as a grid –as shown in figure 6 — with two tokens at certain positions one can introduce a language to describe the ‘facts’ which constitute the state of affairs. In this example one needs ‘names for objects’, ‘properties of objects’ as well as ‘relations between objects’. A possible finite set of facts for situation 1 could be the following:

  1. TOKEN(T1), BLACK(T1), POSITION(T1,1,1)
  2. TOKEN(T2), WHITE(T2), POSITION(T2,2,1)
  3. NEIGHBOR(T1,T2)
  4. CELL(C1), POSITION(1,2), FREE(C1)

‘T1’, ‘T2’, as well as ‘C1’ are names of objects, ‘TOKEN’, ‘BACK’ etc. are names of properties, and ‘NEIGHBOR’ is a relation between objects. This results in the equation:

S1 = {TOKEN(T1), BLACK(T1), POSITION(T1,1,1), TOKEN(T2), WHITE(T2), POSITION(T2,2,1), NEIGHBOR(T1,T2), CELL(C1), POSITION(1,2), FREE(C1)}

These facts describe the situation S1. If it is important to describe possible objects ‘external to the situation’ as important factors which can cause some changes then one can describe these objects as a set of facts  in a separated ‘context’. In this example this could be two players which can move the black and white tokens and thereby causing a change of the situation. What is the situation and what belongs to a context is somewhat arbitrary. If one describes the agriculture of some region one usually would not count the planets and the atmosphere as part of this region but one knows that e.g. the sun can severely influence the situation   in combination with the atmosphere.

Change of a state of affairs given as a state which will be enhanced by a new object
Fig.7: Change of a state of affairs given as a state which will be enhanced by a new object

Let us stay with a state of affairs with only a situation without a context. The state of affairs is     a ‘state’. In the example shown in figure 6 I assume a ‘change’ caused by the insertion of a new black token at position (2,2). Written in the language of facts L_fact we get:

  1. TOKEN(T3), BLACK(T3), POSITION(2,2), NEIGHBOR(T3,T2)

Thus the new state S2 is generated out of the old state S1 by unifying S1 with the set of new facts: S2 = S1 {TOKEN(T3), BLACK(T3), POSITION(2,2), NEIGHBOR(T3,T2)}. All the other facts of S1 are still ‘valid’. In a more general manner one can introduce a change-expression with the following format:

<S1, S2, add(S1,{TOKEN(T3), BLACK(T3), POSITION(2,2), NEIGHBOR(T3,T2)})>

This can be read as follows: The follow-up state S2 is generated out of the state S1 by adding to the state S1 the set of facts { … }.

This layout of a change expression can also be used if some facts have to be modified or removed from a state. If for instance  by some reason the white token should be removed from the situation one could write:

<S1, S2, subtract(S1,{TOKEN(T2), WHITE(T2), POSITION(2,1)})>

Another notation for this is S2 = S1 – {TOKEN(T2), WHITE(T2), POSITION(2,1)}.

The resulting state S2 would then look like:

S2 = {TOKEN(T1), BLACK(T1), POSITION(T1,1,1), CELL(C1), POSITION(1,2), FREE(C1)}

And a combination of subtraction of facts and addition of facts would read as follows:

<S1, S2, subtract(S1,{TOKEN(T2), WHITE(T2), POSITION(2,1)}, add(S1,{TOKEN(T3), BLACK(T3), POSITION(2,2)})>

This would result in the final state S2:

S2 = {TOKEN(T1), BLACK(T1), POSITION(T1,1,1), CELL(C1), POSITION(1,2), FREE(C1),TOKEN(T3), BLACK(T3), POSITION(2,2)}

These simple examples demonstrate another fact: while facts about objects and their properties are independent from each other do relational facts depend from the state of their object facts. The relation of neighborhood e.g. depends from the participating neighbors. If — as in the example above — the object token T2 disappears then the relation ‘NEIGHBOR(T1,T2)’ no longer holds. This points to a hierarchy of dependencies with the ‘basic facts’ at the ‘root’ of a situation and all the other facts ‘above’ basic facts or ‘higher’ depending from the basic facts. Thus ‘higher order’ facts should be added only for the actual state and have to be ‘re-computed’ for every follow-up state anew.

If one would specify a context for state S1 saying that there are two players and one allows for each player actions like ‘move’, ‘insert’ or ‘delete’ then one could make the change from state S1 to state S2 more precise. Assuming the following facts for the context:

  1. PLAYER(PB1), PLAYER(PW1), HAS-THE-TURN(PB1)

In that case one could enhance the change statement in the following way:

<S1, S2, PB1,insert(TOKEN(T3,2,2)),add(S1,{TOKEN(T3), BLACK(T3), POSITION(2,2)})>

This would read as follows: given state S1 the player PB1 inserts a  black token at position (2,2); this yields a new state S2.

With or without a specified context but with regard to a set of possible change statements it can be — which is the usual case — that there is more than one option what can be changed. Some of the main types of changes are the following ones:

  1. RANDOM
  2. NOT RANDOM, which can be specified as follows:
    1. With PROBABILITIES (classical, quantum probability, …)
    2. DETERMINISTIC

Furthermore, if the causing object is an actor which can adapt structurally or even learn locally then this actor can appear in some time period like a deterministic system, in different collected time periods as an ‘oscillating system’ with different behavior, or even as a random system with changing probabilities. This make the forecast of systems with adaptive and/ or learning systems rather difficult.

Another aspect results from the fact that there can be states either with one actor which can cause more than one action in parallel or a state with multiple actors which can act simultaneously. In both cases the resulting total change has eventually to be ‘filtered’ through some additional rules telling what  is ‘possible’ in a state and what not. Thus if in the example of figure 6 both player want to insert a token at position (2,2) simultaneously then either  the rules of the game would forbid such a simultaneous action or — like in a computer game — simultaneous actions are allowed but the ‘geometry of a 2-dimensional space’ would not allow that two different tokens are at the same position.

Another aspect of change is the dimension of time. If the time dimension is not explicitly specified then a change from some state S_i to a state S_j does only mark the follow up state S_j as later. There is no specific ‘metric’ of time. If instead a certain ‘clock’ is specified then all changes have to be aligned with this ‘overall clock’. Then one can specify at what ‘point of time t’ the change will begin and at what point of time t*’ the change will be ended. If there is more than one change specified then these different changes can have different timings.

THIRD PERSON VIEW

Up until now the point of view describing a state and the possible changes of states is done in the so-called 3rd-person view: what can a person perceive if it is part of a situation and is looking into the situation.  It is explicitly assumed that such a person can perceive only the ‘surface’ of objects, including all kinds of actors. Thus if a driver of a car stears his car in a certain direction than the ‘observing person’ can see what happens, but can not ‘look into’ the driver ‘why’ he is steering in this way or ‘what he is planning next’.

A 3rd-person view is assumed to be the ‘normal mode of observation’ and it is the normal mode of empirical science.

Nevertheless there are situations where one wants to ‘understand’ a bit more ‘what is going on in a system’. Thus a biologist can be  interested to understand what mechanisms ‘inside a plant’ are responsible for the growth of a plant or for some kinds of plant-disfunctions. There are similar cases for to understand the behavior of animals and men. For instance it is an interesting question what kinds of ‘processes’ are in an animal available to ‘navigate’ in the environment across distances. Even if the biologist can look ‘into the body’, even ‘into the brain’, the cells as such do not tell a sufficient story. One has to understand the ‘functions’ which are enabled by the billions of cells, these functions are complex relations associated with certain ‘structures’ and certain ‘signals’. For this it is necessary to construct an explicit formal (mathematical) model/ theory representing all the necessary signals and relations which can be used to ‘explain’ the obsrvable behavior and which ‘explains’ the behavior of the billions of cells enabling such a behavior.

In a simpler, ‘relaxed’ kind of modeling  one would not take into account the properties and behavior of the ‘real cells’ but one would limit the scope to build a formal model which suffices to explain the oservable behavior.

This kind of approach to set up models of possible ‘internal’ (as such hidden) processes of an actor can extend the 3rd-person view substantially. These models are called in this text ‘actor models (AM)’.

HIDDEN WORLD PROCESSES

In this text all reported 3rd-person observations are called ‘actor story’, independent whether they are done in a pictorial or a textual mode.

As has been pointed out such actor stories are somewhat ‘limited’ in what they can describe.

It is possible to extend such an actor story (AS)  by several actor models (AM).

An actor story defines the situations in which an actor can occur. This  includes all kinds of stimuli which can trigger the possible senses of the actor as well as all kinds of actions an actor can apply to a situation.

The actor model of such an actor has to enable the actor to handle all these assumed stimuli as well as all these actions in the expected way.

While the actor story can be checked whether it is describing a process in an empirical ‘sound’ way,  the actor models are either ‘purely theoretical’ but ‘behavioral sound’ or they are also empirically sound with regard to the body of a biological or a technological system.

A serious challenge is the occurrence of adaptiv or/ and locally learning systems. While the actor story is a finite  description of possible states and changes, adaptiv or/ and locally learning systeme can change their behavior while ‘living’ in the actor story. These changes in the behavior can not completely be ‘foreseen’!

COGNITIVE EXPERT PROCESSES

According to the preceding considerations a homo sapiens as a biological system has besides many properties at least a consciousness and the ability to talk and by this to communicate with symbolic languages.

Looking to basic modes of an actor story (AS) one can infer some basic concepts inherently present in the communication.

Without having an explicit model of the internal processes in a homo sapiens system one can infer some basic properties from the communicative acts:

  1. Speaker and hearer presuppose a space within which objects with properties can occur.
  2. Changes can happen which presuppose some timely ordering.
  3. There is a disctinction between concrete things and abstract concepts which correspond to many concrete things.
  4. There is an implicit hierarchy of concepts starting with concrete objects at the ‘root level’ given as occurence in a concrete situation. Other concepts of ‘higher levels’ refer to concepts of lower levels.
  5. There are different kinds of relations between objects on different conceptual levels.
  6. The usage of language expressions presupposes structures which can be associated with the expressions as their ‘meanings’. The mapping between expressions and their meaning has to be learned by each actor separately, but in cooperation with all the other actors, with which the actor wants to share his meanings.
  7. It is assume that all the processes which enable the generation of concepts, concept hierarchies, relations, meaning relations etc. are unconscious! In the consciousness one can  use parts of the unconscious structures and processes under strictly limited conditions.
  8. To ‘learn’ dedicated matters and to be ‘critical’ about the quality of what one is learnig requires some disciplin, some learning methods, and a ‘learning-friendly’ environment. There is no guaranteed method of success.
  9. There are lots of unconscious processes which can influence understanding, learning, planning, decisions etc. and which until today are not yet sufficiently cleared up.

 

 

 

 

 

 

 

 

ADVANCED AAI-THEORY

eJournal: uffmm.org,
ISSN 2567-6458, 21.Januar 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

Here You can find a new version of this post

CONTEXT

The last official update of the AAI theory dates back to Oct-2, 2018. Since that time many new thoughts have been detected and have been configured for further extensions and improvements. Here I try to give an overview of all the actual known aspects of the expanded AAI theory as a possible guide for the further elaborations of the main text.

CLARIFYING THE PROBLEM

  1. Generally it is assumed that the AAI theory is embedded in a general systems engineering approach starting with the clarification of a problem.
  2. Two cases will be distinguished:
    1. A stakeholder is associated with a certain domain of affairs with some prominent aspect/ parameter P and the stakeholder wants to clarify whether P poses some ‘problem’ in this domain. This presupposes some explained ‘expectations’ E how it should be and some ‘findings’ x pointing to the fact that P is ‘sufficiently different’ from some y>x. If the stakeholder judges that this difference is ‘important’, than P matching x will be classified as a problem, which will be documented in a ‘problem document D_p’. One interpret this this analysis as a ‘measurement M’ written as M(P,E) = x and x<y.
    2. Given a problem document D_p a stakeholder invites some experts to find a ‘solution’ which transfers the old ‘problem P’ into a ‘configuration S’ which at least should ‘minimize the problem P’. Thus there must exist some ‘measurements’ of the given problem P with regard to certain ‘expectations E’ functioning as a ‘norm’ as M(P,E)=x and some measurements of the new configuration S with regard to the same expectations E as M(S,E)=y and a metric which allows the judgment y > x.
  3. From this follows that already in the beginning of the analysis of a possible solution one has to refer to some measurement process M, otherwise there exists no problem P.

CHECK OF FRAMING CONDITIONS

  1. The definition of a problem P presupposes a domain of affairs which has to be characterized in at least two respects:
    1. A minimal description of an environment ENV of the problem P and
    2. a list of so-called non-functional requirements (NFRs).
  2. Within the environment it mus be possible to identify at least one task T to be realized from some start state to some end state.
  3. Additionally it mus be possible to identify at least one executing actor A_exec doing this task and at least one actor assisting A_ass the executing actor to fulfill the task.
  4. For the  following analysis of a possible solution one can distinguish two strategies:
    1. Top-down: There exists a group of experts EXPs which will analyze a possible solution, will test these, and then will propose these as a solution for others.
    2. Bottom-up: There exists a group of experts EXPs too but additionally there exists a group of customers CTMs which will be guided by the experts to use their own experience to find a possible solution.

ACTOR STORY (AS)

  1. The goal of an actor story (AS) is a full specification of all identified necessary tasks T which lead from a start state q* to a goal state q+, including all possible and necessary changes between the different states M.
  2. A state is here considered as a finite set of facts (F) which are structured as an expression from some language L distinguishing names of objects (LIKE ‘d1’, ‘u1’, …) as well as properties of objects (like ‘being open’, ‘being green’, …) or relations between objects (like ‘the user stands before the door’). There can also e a ‘negation’ like ‘the door is not open’. Thus a collection of facts like ‘There is a door D1’ and ‘The door D1 is open’ can represent a state.
  3. Changes from one state q to another successor state q’ are described by the object whose action deletes previous facts or creates new facts.
  4. In this approach at least three different modes of an actor story will be distinguished:
    1. A pictorial mode generating a Pictorial Actor Story (PAS). In a pictorial mode the drawings represent the main objects with their properties and relations in an explicit visual way (like a Comic Strip).
    2. A textual mode generating a Textual Actor Story (TAS): In a textual mode a text in some everyday language (e.g. in English) describes the states and changes in plain English. Because in the case of a written text the meaning of the symbols is hidden in the heads of the writers it can be of help to parallelize the written text with the pictorial mode.
    3. A mathematical mode generating a Mathematical Actor Story (MAS): n the mathematical mode the pictorial and the textual modes are translated into sets of formal expressions forming a graph whose nodes are sets of facts and whose edges are labeled with change-expressions.

TASK INDUCED ACTOR-REQUIREMENTS (TAR)

If an actor story AS is completed, then one can infer from this story all the requirements which are directed at the executing as well as the assistive actors of the story. These requirements are targeting the needed input- as well as output-behavior of the actors from a 3rd person point of view (e.g. what kinds of perception are required, what kinds of motor reactions, etc.).

ACTOR INDUCED ACTOR-REQUIREMENTS (AAR)

Depending from the kinds of actors planned for the real work (biological systems, animals or humans; machines, different kinds of robots), one has to analyze the required internal structures of the actors needed to enable the required perceptions and responses. This has to be done in a 1st person point of view.

ACTOR MODELS (AMs)

Based on the AARs one has to construct explicit actor models which are fulfilling the requirements.

USABILITY TESTING (UTST)

Using the actor as a ‘norm’ for the measurement one has to organized an ‘usability test’ in he way, that a real executing test actor having the required profiles has to use a real assisting actor in the context of the specified actor story. Place in a start state of the actor story the executing test actor has to show that and how he will reach the defined goal state of the actor story. For this he has to use a real assistive actor which usually is an experimental device (a mock-up), which allows the test of the story.

Because an executive actor is usually a ‘learning actor’ one has to repeat the usability test n-times to see, whether the learning curve approaches a minimum. Additionally to such objective tests one should also organize an interview to get some judgments about the subjective states of the test persons.

SIMULATION

With an increasing complexity of an actor story AS it becomes important to built a simulator (SIM) which can take as input the start state of the actor story together with all possible changes. Then the simulator can compute — beginning with the start state — all possible successor states. In the interactive mode participating actors will explicitly be asked to interact with the simulator.

Having a simulator one can use a simulator as part of an usability test to mimic the behavior of an assistive actor. This mode can also be used for training new executive actors.

A TOP-DOWN ACTOR STORY

The elaboration of an actor story will usually be realized in a top-down style: some AAI experts will develop the actor story based on their experience and will only ask for some test persons if they have elaborated everything so far that they can define some tests.

A BOTTOM-UP ACTOR STORY

In a bottom-up style the AAI experts collaborate from the beginning with a group of common users from the application domain. To do this they will (i) extract the knowledge which is distributed in the different users, then (ii) they will start some modeling from these different facts to (iii) enable some basic simulations. This simple simulation (iv) will be enhanced to an interactive simulation which allows serious gaming either (iv.a) to test the model or to enable the users (iv.b) to learn the space of possible states. The test case will (v) generate some data which can be used to evaluate the model with regard to pre-defined goals. Depending from these findings (vi) one can try to improve the model further.

THE COGNITIVE SPACE

To be able to construct executive as well as assistive actors which are close to the way how human persons do communicate one has to set up actor models which are as close as possible with the human style of cognition. This requires the analysis of phenomenal experience as well as the psychological behavior as well as the analysis of a needed neuron-physiological structures.

STATE DYNAMICS

To model in an actor story the possible changes from one given state to another one (or to many successor states) one needs eventually besides explicit deterministic changes different kinds of random rules together with adaptive ones or decision-based behavior depending from a whole network of changing parameters.

LIBRARIES AS ACTORS. WHAT ABOUT THE CITIZENS?

eJournal: uffmm.org, ISSN 2567-6458, 19.Januar 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

In this blog a new approach to the old topic of ‘Human-Machine Interaction (HMI)’ is developed turning the old Human-Machine dyad into the many-to-many relation of ‘Actor-Actor Interaction (AAI)’. And, moreover, in this new AAI approach the classical ‘top-down’ approach of engineering is expanded with a truly ‘bottom-up’ approach locating the center of development in the distributed knowledge of a population of users assisted by the AAI experts.

PROBLEM

From this perspective it is interesting to see how on an international level the citizens of a community/ city are not at the center of research, but again the city and its substructures – here public libraries – are called ‘actors’ while the citizens as such are only an anonymous matter of driving these structures to serve the international ‘buzz word’ of a ‘smart city’ empowered by the ‘Internet of Things (IoT)’.

This perspective is published in a paper from Shannon Mersand et al. (2019) which reviews all the main papers available focusing on the role of public libraries in cities. It seems – I could not check by myself the search space — that the paper gives a good overview of this topic in 48 cited papers.

The main idea underlined by the authors is that public libraries are already so-called ‘anchor institutions’ in a community which either already include or could be extended as “spaces for innovation, collaboration and hands on learning that are open to adults and younger children as well”. (p.3312) Or, another formulation “that libraries are consciously working to become a third space; a place for learning in multiple domains and that provides resources in the form of both materials and active learning opportunities”. (p.3312)

The paper is rich on details but for the context of the AAI paradigm I am interested only on the general perspective how the roles of the actors are described which are identified as responsible for the process of problem solving.

The in-official problem of cities is how to organize the city to respond to the needs of its citizens. There are some ‘official institutions’ which ‘officially’ have to fulfill this job. In democratic societies these institutions are ‘elected’. Ideally these official institutions are the experts which try to solve the problem for the citizens, which are the main stakeholder! To help in this job of organizing the ‘best fitting city-layout’ there exists usually at any point of time a bunch of infrastructures. The modern ‘Internet of Things (IoT)’ is only one of many possible infrastructures.

To proceed in doing the job of organizing the ‘best fitting city-layout’ there are generally two main strategies: ‘top-down’ as usual in most cities or ‘bottom-‘ in nearly no cities.

In the top-down approach the experts organize the processes of the cities more or less on their own. They do not really include the expertise of their citizens, not their knowledge, not their desires and visions. The infrastructures are provided from a birds perspective and an abstract systems thinking.

The case of the public libraries is matching this top-down paradigm. At the end of their paper the authors classify public libraries not only as some ‘infrastructure’ but “… recognize the potential of public libraries … and to consider them as a key actor in the governance of the smart community”. (p.3312) The term ‘actor’ is very strong. This turns an institution into an actor with some autonomy of deciding what to do. The users of the library, the citizens, the primary stakeholder of the city, are not seen as actors, they are – here – the material to ‘feed’ – to use a picture — the actor library which in turn has to serve the governance of the ‘smart community’.

DISCUSSION

Yes, this comment can be understood as a bit ‘harsh’ because one can read the text of the authors a bit different in the sense that the citizens are not only some matter to ‘feed’ the actor library but to see the public library as an ‘environment’ for the citizens which find in the libraries many possibilities to learn and empower themselves. In this different reading the citizens are clearly seen as actors too.

This different reading is possible, but within an overall ‘top-down’ approach the citizens as actors are not really included as actors but only as passive receivers of infrastructure offers; in a top-down approach the main focus are the infrastructures, and from all the infrastructures the ‘smart’ structures are most prominent, the internet of things.

If one remembers two previous papers of Mila Gascó (2016) and Mila Gascó-Hernandez (2018) then this is a bit astonishing because in these earlier papers she has analyzed that the ‘failure’ of the smart technology strategy in Barcelona was due to the fact that the city government (the experts in our framework) did not include sufficiently enough the citizens as actors!

From the point of view of the AAI paradigm this ‘hiding of the citizens as main actors’ is only due to the inadequate methodology of a top-down approach where a truly bottom-up approach is needed.

In the Oct-2, 2018 version of the AAI theory the bottom-up approach is not yet included. It has been worked out in the context of the new research project about ‘City Planning and eGaming‘  which in turn has been inspired by Mila Gascó-Hernandez!

REFERENCES

  • S.Mersand, M. Gasco-Hernandez, H. Udoh, and J.R. Gil-Garcia. “Public libraries as anchor institutions in smart communities: Current practices and future development”, Proceedings of the 52nd Hawaii International Conference on System Sciences, pages 3305 – 3314, 2019. URL https: //hdl.handle.net/10125/59766 .

  • Mila Gascó, “What makes a city smart? lessons from Barcelona”. 2016 49th Hawaii International Conference on System Sciences (HICSS), pages 2983–2989, Jan 2016. D O I : 10.1109/HICSS.2016.373.

  • Mila Gascó-Hernandez, “Building a smart city: Lessons from Barcelona.”, Commun. ACM, 61(4):50–57, March 2018. ISSN 0001-0782. D O I : 10.1145/3117800. URL http://doi.acm.org/10.1145/3117800 .

ACTOR-ACTOR INTERACTION [AAI] WITHIN A SYSTEMS ENGINEERING PROCESS (SEP). An Actor Centered Approach to Problem Solving

eJournal: uffmm.org, ISSN 2567-6458
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

ATTENTION: The actual Version  you will find HERE.

Draft version 22.June 2018

Update 26.June 2018 (Chapter AS-AM Summary)

Update 4.July 2018 (Chapter 4 Actor Model; improving the terminology of environments with actors, actors as input-output systems, basic and real interface, a first typology of input-output systems…)

Update 17.July 2018 (Preface, Introduction new)

Update 19.July 2018 (Introduction final paragraph!, new chapters!)

Update 20.July 2018 (Disentanglement of chapter ‘Simulation & Verification’ into two independent chapters; corrections in the chapter ‘Introduction’; corrections in chapter ‘AAI Analysis’; extracting ‘Simulation’ from chapter ‘Actor Story’ to new chapter ‘Simulation’; New chapter ‘Simulation’; Rewriting of chapter ‘Looking Forward’)

Update 22.July 2018 (Rewriting the beginning of the chapter ‘Actor Story (AS)’, not completed; converting chapter ‘AS+AM Summary’ to ‘AS and AM Philosophy’, not completed)

Update 23.July 2018 (Attaching a new chapter with a Case Study illustrating an actor story (AS). This case study is still unfinished. It is a case study of  a real project!)

Update 7.August 2018 (Modifying chapter Actor Story, the introduction)

Update 8.August 2018 (Modifying chapter  AS as Text, Comic, Graph; especially section about the textual mode and the pictorial mode; first sketch for a mapping from the textual mode into the pictorial mode)

Update 9.August 2018 (Modification of the section ‘Mathematical Actor Story (MAS) in chapter 4).

Update 11.August 2018 (Improving chapter 3 ‘Actor Story; nearly complete rewriting of chapter 4 ‘AS as text, comic, graph’.)

Update 12.August 2018 (Minor corrections in the chapters 3+4)

Update 13.August 2018 (I am still catched by the chapters 3+4. In chapter  the cognitive structure of the actors has been further enhanced; in chapter 4 a complete example of a mathematical actor story could now been attached.)

Update 14.August 2018 (minor corrections to chapter 4 + 5; change-statements define for each state individual combinatorial spaces (a little bit like a quantum state); whether and how these spaces will be concretized/ realized depends completely from the participating actors)

Update 15.August 2018 (Canceled the appendix with the case study stub and replaced it with an overview for  a supporting software tool which is needed for the real usage of this theory. At the moment it is open who will write the software.)

Update 2.October 2018 (Configuring the whole book now with 3 parts: I. Theory, II. Application, III. Software. Gerd has his focus on part I, Zeynep will focus on part II and ‘somebody’ will focus on part III (in the worst case we will — nevertheless — have a minimal version :-)). For a first quick overview about everything read the ‘Preface’ and the ‘Introduction’.

Update 4.November 2018 (Rewriting the Introduction (and some minor corrections in the Preface). The idea of the rewriting was to address all the topics which will be discussed in the book and pointing out to the logical connections between them. This induces some wrong links in the following chapters, which are not yet updated. Some chapters are yet completely missing. But to improve the clearness of the focus and the logical inter-dependencies helps to elaborate the missing texts a lot. Another change is the wording of the title. Until now it is difficult to find a title which is exactly matching the content. The new proposal shows the focus ‘AAI’ but lists the keywords of the main topics within AAA analysis because these topics are usually not necessarily associated with AAI.)

ACTOR-ACTOR INTERACTION [AAI]. An Actor Centered Approach to Problem Solving. Combining Engineering and Philosophy

by

GERD DOEBEN-HENISCH in cooperation with  LOUWRENCE ERASMUS, ZEYNEP TUNCER

LATEST  VERSION AS PDF

BACKGROUND INFORMATION 19.Dec.2018: Application domain ‘Communal Planning and e-Gaming’

BACKGROUND INFORMATION 24.Dec.2018: The AAI-paradigm and Quantum Logic

PRE-VIEW: NEW EXPANDED AAI THEORY 23.January 2019: Outline of the new expanded  AAI Paradigm. Before re-writing the main text with these ideas the new advanced AAI theory will first be tested during the summer 2019 within a lecture with student teams as well as in  several workshops outside the Frankfurt University of Applied Sciences with members of different institutions.

AASE – Actor-Actor Systems Engineering. Theory & Applications. Micro-Edition (Vers.9)

eJournal: uffmm.org, ISSN 2567-6458
13.June  2018
Email: info@uffmm.org
Authors: Gerd Doeben-Henisch, Zeynep Tuncer,  Louwrence Erasmus
Email: doeben@fb2.fra-uas.de
Email: gerd@doeben-henisch.de

PDF

CONTENTS

1 History: From HCI to AAI …
2 Different Views …
3 Philosophy of the AAI-Expert …
4 Problem (Document) …
5 Check for Analysis …
6 AAI-Analysis …
6.1 Actor Story (AS) . . . . . . . . . . . . . . . . . . . . . . . . .
6.1.1 Textual Actor Story (TAS) . . . . . . . . . . . . . . .
6.1.2 Pictorial Actor Story (PAT) . . . . . . . . . . . . . .
6.1.3 Mathematical Actor Story (MAS) . . . . . . . . . . .
6.1.4 Simulated Actor Story (SAS) . . . . . . . . . . . . .
6.1.5 Task Induced Actor Requirements (TAR) . . . . . . .
6.1.6 Actor Induced Actor Requirements (UAR) . . . . . .
6.1.7 Interface-Requirements and Interface-Design . . . .
6.2 Actor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2.1 Actor and Actor Story . . . . . . . . . . . . . . . . .
6.2.2 Actor Model . . . . . . . . . . . . . . . . . . . . . .
6.2.3 Actor as Input-Output System . . . . . . . . . . . .
6.2.4 Learning Input-Output Systems . . . . . . . . . . . .
6.2.5 General AM . . . . . . . . . . . . . . . . . . . . . .
6.2.6 Sound Functions . . . . . . . . . . . . . . . . . . .
6.2.7 Special AM . . . . . . . . . . . . . . . . . . . . . .
6.2.8 Hypothetical Model of a User – The GOMS Paradigm
6.2.9 Example: An Electronically Locked Door . . . . . . .
6.2.10 A GOMS Model Example . . . . . . . . . . . . . . .
6.2.11 Further Extensions . . . . . . . . . . . . . . . . . .
6.2.12 Design Principles; Interface Design . . . . . . . . .
6.3 Simulation of Actor Models (AMs) within an Actor Story (AS) .
6.4 Assistive Actor-Demonstrator . . . . . . . . . . . . . . . . . .
6.5 Approaching an Optimum Result . . . . .
7 What Comes Next: The Real System
7.1 Logical Design, Implementation, Validation . . . .
7.2 Conceptual Gap In Systems Engineering? . . .
8 The AASE-Paradigm …
References

Abstract

This text is based on the the paper “AAI – Actor-Actor Interaction. A Philosophy of Science View” from 3.Oct.2017 and version 11 of the paper “AAI – Actor-Actor Interaction. An Example Template” and it   transforms these views in the new paradigm ‘Actor- Actor Systems Engineering’ understood as a theory as well as a paradigm for and infinite set of applications. In analogy to the slogan ’Object-Oriented Software Engineering (OO SWE)’ one can understand the new acronym AASE as a systems engineering approach where the actor-actor interactions are the base concepts for the whole engineering process. Furthermore it is a clear intention to view the topic AASE explicitly from the point of view of a theory (as understood in Philosophy of Science) as well as from the point of view of possible applications (as understood in systems engineering). Thus the classical term of Human-Machine Interaction (HMI) or even the older Human-Computer Interaction (HCI) is now embedded within the new AASE approach. The same holds for the fuzzy discipline of Artificial Intelligence (AI) or the subset of AI called Machine Learning (ML). Although the AASE-approach is completely in its beginning one can already see how powerful this new conceptual framework  is.

 

 

AAI – Actor-Actor Interaction. A Toy-Example, No.1

eJournal: uffmm.org, ISSN 2567-6458
13.Dec.2017
Email: info@uffmm.org

Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

Contents

1 Problem ….. 3
2 AAI-Check ….. 3
3 Actor-Story (AS) …..  3
3.1 AS as a Text . . . . . . . . . . . . . . . . . .3
3.2 Translation of a Textual AS into a Formal AS …… 4
3.3 AS as a Formal Expression . . . . . . . . . .4
3.4 Translation of a Formal AS into a Pictorial AS… 5
4 Actor-Model (AM) …..  5
4.1 AM for the User as a Text . . . . . . . . . . . . . . . . . . . . . . . . . .  . . . .6
4.2 AM for the System as a Text . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
5 Combined AS and AM as a Text …..  6
5.1 AM as an Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
6 Simulation …..  7
6.1 Simulating the AS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7
6.2 Simulating the AM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7
6.3 Simulating AS with AM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
7 Appendix: Formalisms ….. 8
7.1 Set of Strings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9
7.2 Predicate Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
8 Appendix: The Meaning of Expressions …11
8.1 States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
8.2 Changes by Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Abstract

Following the general concepts of the paper ’AAI – Actor-Actor Interaction. A Philosophy of Science View’ from 3.Oct.2017 this paper illustrates a simple application where the difference as well as the
interaction between an actor story and several actor models is shown. The details of interface-design as well as the usability-testing are not part of this example.(This example replaces the paper with the title
’AAI – Case Study Actor Story with Actor Model. Simple Grid-Environment’ from 15.Nov.2017). One special point is the meaning of the formal expressions of the actor story.

Attention: This toy example is not yet in fully conformance with the newly published Case-Study-Template

To read the full text see PDF

Clearly, one can debate whether a ‘toy-example’ makes sens, but the complexity of the concepts in this AAI-approach is to great to illustrate these in the beginning  with a realistic example without loosing the idea. The author of the paper has tried many — also very advanced — versions in the last years and this is the first time that he himself has the feeling that at least the idea is now clear enough. And from teaching students it is very clear, if you cannot explain an idea in a toy-example you never will be able to apply it to real big problems…

 

INTELLIGENT MACHINES – INTRODUCTION

eJournal: uffmm.org,
ISSN 2567-6458, 09.Oct 2017 – April 9, 2022, 13:30 h
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

 Remark April 2022

This post from Oct 2017 will be reviewed in the new conceptual framework of an Applied Empirical Theory [AET] with an additional Dynamic Format [DF]. For more details see HERE.

OVERVIEW

A short story telling You, (i) how we interface the intelligent machines (IM) part with the actor-actor interaction (AAI) part, (ii) a first working definition of intelligent machines (IM) in this text, and (iii) defining intelligence and how one can this measure.

IM WITHIN AAI

In this blog we see IM not isolated, as a stand alone endeavor, but as embedded in a discipline called actor-actor interaction (AAI)(later called DAAI := Distributed Actor Actor Interaction).  AAI investigates complex tasks and looks how different kinds of actors are interacting in these contexts with technical systems. As far as the participating systems have been technical systems one speaks here of a system interface (SI) as that part of a technical system, which is interacting with the human actor. In the case of biological systems (mostly humans, but it could be animals as well), one speaks of the user interface (UI). In this text we generalize both cases by the general concept of an actor — biological and non-biological –, which has some actor interface (ActI), and this actor interface embraces all properties which are relevant for the interactions of the actor.

For the analysis of the behavior of actors in such task-environments one can distinguish two important concepts: the actor story (AS) describing the context as an observable process, as well as different actor models (AM). Actor models are special extensions of an actor story because an actor model describes the observable behavior of actors as a behavior function (BF) with a set of assumptions about possible internal states of the actors. The assumptions about possible internal states (IS) are either completely arbitrary or empirically motivated.

The embedding of IM within AAI can be realized through the concept of an actor model (UM) and the actor story (AS). Whatever is important for something which is called an intelligent machine application (IMA) can be defined as an actor model within an actor story. This embedding of IM within AAI offers many advantages.

This has to be explained with some more details.

An Intelligent Machine (IM) in an Actor Story

Let us assume that there exists a mathematical-graph representation of an actor story written as AS_{L_{ε}}. Such a graph has nodes which represent situations. Formally these are sets of properties, probably more fine-grained by subsets which represent different kinds of actors embedded in this situation as well as different kinds of non-actors.

Actors can be classified (as introduced above) as either biological actors (BA) or non-biological actors (NBA). Both kinds of actors can — in another reading — be subsumed under the general term of input-output-systems (IO-SYS). An input-output system can be a learning system or non-learning. Another basic property is that of being intelligent or non-intelligent. Being a learning system and being an intelligent system is usually strongly connected, but this must not necessarily be so. Being a learning system can be associated with being non-intelligent and being intelligent can be connected with being non-learning.(cf. Figure 1)

Classification of input-output systems according to learning, intelligence and beeing biological or not biological
Classification of input-output systems according to learning, intelligence and being biological or non-biological

While biological systems are always learning and intelligent, one can find non-biological systems of all types: non-learning and non-intelligent, non-intelligent and learning, non-learning and intelligent, and learning and intelligent.

Learning System

To classify a system as a learning system this requires the general ability to change the behavior of this system in time thus that there exists a time-span (t1,t2) after which the behavior as response to  certain critical stimuli has changed compared to the time before. [1] From this requirement it follows, that a learning system is an input-output system with at least one internal state which can change. Thus we have the general assumption:

Def: Learning System (LS)

  1. LS(x) iff
  2. x=<I, O, IS, phi >
  3. φ : I x IS —> IS x O
  4. I := Input
  5. O := Output
  6. IS := Internal states

Some x is a learning system (LS) if it is a structure containing sets for input (I), Output (O), as well as internal states (IS). These sets are operated by a behavior function φ which maps inputs and actual internal states to output as well as back to internal states. The set of possible learning functions is infinite.

Intelligent System

The term ‘intelligent’ and ‘intelligence’ is until now not standardized. This means that everybody is using it at little bit arbitrarily.

In this text we take the basic idea of a scientific usage of the term ‘intelligence’ from experimental psychology, which has developed clearly defined operational concepts since the end of the 19. Century which have been proved as quite stable in their empirical applications. [2a,b,c] 

The central idea of the psychological concept of the usage of the term ‘intelligence’ is to associate the usage of the term ‘intelligence’ with observable behavior of those actors, which shall be classified according defined methods of measurement.

In the case of experimental psychology the actors have been biological systems, mainly humans, in the first years of the research school children of certain ages. Because nobody did know what ‘intelligence’ means ‘as such’ one agreed to accept the observable behavior of children in certain task environments as ‘manifestations’ of a ‘presupposed unknown intelligence’. Thus the ability of children to solve defined tasks in a certain defined manner became a norm for what is called ‘intelligence’. Solving the tasks in a certain time with less than a certain amount of errors was used as a ‘baseline’ and all behavior deviating from the baseline was ‘better’ or ‘poorer’.

Thus the ‘content’ of the ‘meaning’ of the term ‘intelligence’ has been delegated to historical patterns of behavior which were common in a certain time-span in a certain geographical and cultural region.

While these behavior patterns can change during the course of time the general method of measurement is invariant.

In the time since then experimental psychology has modified and elaborated this first concept in some directions.

One direction is the modification of the kind of tasks which are used for the tests. With regard to the cultural context one has modified the content, thereby looking to find such kinds of task which seem to be ‘invariant’ with regard to the presupposed intelligence factor. This is an ongoing process.

The other direction is the focus on the actors as such. Because biological systems like humans change the development of their intelligence with age one has tried to find out ‘typical tasks for every age’. This too is an ongoing process.

This history of experimental psychology gives very interesting examples how one can approach the problem of the usage and the measurement of some X which we call ‘intelligence’.

In the context of an AAI-approach we have not only biological systems, but also non-biological systems. Thus most of the elaborated parameters of psychology for human actors are not general enough.

One possible strategy to generalize the intelligence-paradigm of experimental psychology could be to ‘free’ the selection of task sets from the narrow human cultures of the past and require only ‘clearly defined task sets with defined interfaces and defined contexts’. All these tasks sets can be arranged either in one super-set or in a parameterized field of sets. The sum of all these sets defines then a space of possible behavior and associated with this a space of possible measurable intelligence.

A task has then to be given as an actor story according to the AAI-paradigm. Such a specified actor story allows the formal definition of a complexity measure which can be used to measure the ‘amount of intelligence necessary to solve such a task’.

With such a more general and extendable approach to the measurement of observable intelligence one can compare all kinds of systems with each other. With such an approach one can further show objectively, where biological and non-biological systems differ generally, where they are similar, and to which extend they differ with regard to concrete circumstances.

Measuring Intelligence by Actor Stories

Presupposing actor stories (AS) (ideally formalized as mathematical graphs) on can define a first operational general measurement of intelligence.

Def: Task-Intelligence of a task τ (TInt(τ))

    1. Every defined task τ represents a graph g with one shortest path pmin(τ)= π_{min} from a start node to a goal node.
    2. Every such shortest path π_{min} has a certain number of nodes path-nodes(π_{min})=ν.
    3. The number of solved nodes (ν_{solved}) can become related against the total number of nodes ν as ν_{solved}/ν. We take TInt(τ)= ν_{solved}/ν. It follows that TInt(τ) is between 0 and 1: 0 ≤ TInt(τ)≤ 1.
    4. To every task  a maximal duration Δ_{max} is attached; all nodes which are solved within this maximal duration time Δ_{max} are declared as ‘solved’, all the others as ‘un-solved’.

The usual case will require more than one task to be realized. Thus we introduce the concept of a task field (TF).

Def: Task-Field of type x (TF_{x})
Def: Task-Field Intelligence (TFInt)

A task-field TF of type x includes a finite set of individual tasks like TF_{x} = { τ{x.1}, τ{x.2}, … , τ{x.n} } with n ≥ 2. The sum of all individual task intelligence values TInt(τ{x.i}) has to be normalized to 1, i.e. (TInt(τ{x.1}) + TInt(τ{x.2}) + … + TInt(τ{x.n}))/ n (with 0 in the nominator not allowed). Thus the value of the intelligence of a task field of type x TFInt(TF_{x}) is again in the domain of [0,1].

Because the different tasks in a task field TF can be of different difficulty it should be possible to introduce some weighting for the individual task intelligence values. This should not change the general mechanism.

Def: Combined Task-Fields (TF)

In face of the huge variety of possible task fields in this world it can make sens to introduce more general layers by grouping task fields of different types together to larger combined fields, like TF_{x,…,z} = TF_{x} ∪ TF_{y} ∪ … ∪ TF_{z}. The task field intelligence TFInt of such combined task fields would be computed as before.

Def: Omega Task-Field at time t (TF_{ω}(t))

The most comprehensive assembly of such combinations shall here be called the Omega-Task-Field at time t TF_{ω}(t). This indicates the known maximum of intelligence measurements at that point of time.

Measurement Comments

With these assumptions the term intelligence will be restricted to clearly defined domains either to an individual task, to a task-field of type x, or to some grouped task-fields or being related to the actual omega task-field. In every such domain the intelligence value is in the realm of [0,1] or written as some value between 0 or 100%.

Independent of the type of an actor — biological or not — one can measure the intelligence of such an actor with the same domains of defined tasks. As a result one can easily compare all known actors with regard to such defined task domains.

Because the acting actors can be quite different by their input-output capabilities it follows that every actor has to organize some interface which enables him to use the defined task. There are no special restrictions to the format of such an interface, but there is one requirement which has to be observed strictly: the interface as such is not allowed to do any kind of computation beyond providing only the necessary input from the task domain or to provide the necessary output to the domain. Only then are the different tests able to reveal some difference between the different actors.

If the tests show differences between certain types of actors with regard to a certain task or a task-field then this is a chance to develop smart assistive interfaces which can help the actor in question to overcome his weakness compared to the other type of actor. Thus this kind of measuring intelligence can be a strong supporter for a better world in the future.

Another consequence of the differing intelligence values can be to look to the inner structure of an actor with weaker values and asking how one could improve his capabilities. This can be done e.g. by different kinds of training or by improving his system structures.

COMMENTS

[1] Sara J.Shettleworth, Biological Approaches to the Study of Learning, pp.185 – 219, in: N.J.Mackintosh (Ed.), Animal Learning and Cognition, Academic Press, San Diego, New York, London et.al., 1994

[2a] Ernest R.Hilgard, Rita L.Atkinson, Richard C.Atkinson, Introduction to Psychology, Harcourt Brace Jovanovic, Inc., Psychology, 7th ed., New York, San Diego, Chicago et.al, 1979

[2b] Detlef H.Rost, Intelligenz. Fakten und Mythen, Belz Verlag, Weinheim – Basel, 2009

[2c] Detlef H.Rost, Handbuch Intelligenz, Beltz Verlag, Weinheim – Basel, 2013