The analysis of the main application scenario revealed that classical
logical inference concepts are insufficient for the assistance of human ac-
tors during shared planning. It turned out that the simulator has to be
understood as a real learning artificial actor which has to gain the required
knowledge during the process.
In this section several case studies will be presented.In Case Study 1 it will be shown, how the DAAI paradigm can be mapped into a new concept of Generative Cultural Anthropology [GCA]. Then it will be shown how it is possible to implement this GCA in a way which allows all kinds of cultural processes including those dealing with city planning and the participation of citizens in this process. The working title of the software project related to this is komega. Go back to the overall framework.
COLLECTION OF PAPERS
(The order of the different papers represents rather the logical not the timely order of the project. The most recent papers reflect the most actual perspective.)
FROM DAAI to GCA. Turning Engineering into Generative Cultural Anthropology. This paper gives an outline how one can map the DAAI paradigm directly into the GCA paradigm (April-19,2020): case1-daai-gca-v1
A first GCA open research project [GCA-OR No.1]. This paper outlines a first open research project using the GCA. This will be the framework for the first implementations (May-5, 2020): GCAOR-v0-1
Engineering and Society. A Case Study for the DAAI Paradigm – Introduction. This paper illustrates important aspects of a cultural process looking to the acting actors where certain groups of people (experts of different kinds) can realize the generation, the exploration, and the testing of dynamical models as part of a surrounding society. Engineering is clearly not separated from society (April-9, 2020): case1-population-start-part0-v1
Bootstrapping some Citizens. This paper clarifies the set of general assumptions which can and which should be presupposed for every kind of a real world dynamical model (April-4, 2020): case1-population-start-v1-1
Hybrid Simulation Game Environment [HSGE]. This paper outlines the simulation environment by combing a usual web-conference tool with an interactive web-page by our own (23.May 2020): HSGE-v2 (May-5, 2020): HSGE-v0-1
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
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
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.
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.
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.
Last change: 28.February 2019 (Several corrections)
An overview to the enhanced AAI theory version 2 you can find here. In this post we talk about the special topic how to proceed in a bottom-up approach.
BOTTOM-UP: THE GENERAL BLUEPRINT
As the introductory figure shows it is assumed here that there is a collection of citizens and experts which offer their individual knowledge, experiences, and skills to ‘put them on the table’ challenged by a given problem P.
This knowledge is in the beginning not structured. The first step in the direction of an actor story (AS) is to analyze the different contributions in a way which shows distinguishable elements with properties and relations. Such a set of first ‘objects’ and ‘relations’ characterizes a set of facts which define a ‘situation’ or a ‘state’ as a collection of ‘facts’. Such a situation/ state can also be understood as a first simple ‘model‘ as response to a given problem. A model is as such ‘static‘; it describes what ‘is’ at a certain point of ‘time’.
In a next step the group has to identify possible ‘changes‘ which can be associated with at least one fact. There can be many possible changes which eventually need different durations to come into effect. These effects can happen as ‘exclusive alternatives’ or in ‘parallel’. Apply the possible changes to a situation generates ‘successors’ to the actual situation. A sequence of situations generated by applied changes is usually called a ‘simulation‘.
If one allows the interaction between real actors with a simulation by associating a real actor to one of the actors ‘inside the simulation’ one is turning the simulation into an ‘interactive simulation‘ which represents basically a ‘computer game‘ (short: ‘egame‘).
One can use interactive simulations e.g. to (i) learn about the dynamics of a model, to (ii) test the assumptions of a model, to (iii) test the knowledge and skills of the real actors.
Making new experiences with a simulation allows a continuous improvement of the model and its change rules.
Additionally one can include more citizens and experts into this process and one can use available knowledge from databases and libraries.
EPISTEMOLOGY OF CONCEPTS
As outlined in the preceding section about the blueprint of a bottom-up process there will be a heavy usage of concepts to describe state of affairs.
The literature about this topic in philosophy as well as many scientific disciplines is overwhelmingly and therefore this small text here can only be a ‘pointer’ into a complex topic. Nevertheless I will use exactly this pointer to explore this topic further.
While the literature is mainly dealing with more or less specific partial models, I am trying here to point out a very general framework which fits to a more genera philosophical — especially epistemological — view as well as gives respect to many results of scientific disciplines.
The main dimensions here are (i) the outside external empirical world, which connects via sensors to the (ii) internal body, especially the brain, which works largely ‘unconscious‘, and then (iii) the ‘conscious‘ part of he brain.
The most important relationship between the ‘conscious’ and the ‘unconscious’ part of the brain is the ability of the unconscious brain to transform automatically incoming concrete sens-experiences into more ‘abstract’ structures, which have at least three sub-dimensions: (i) different concrete material, (ii) a sub-set of extracted common properties, (iii) different sets of occurring contexts associated with the different subsets. This enables the brain to extract only a ‘few’ abstract structures (= abstract concepts) to deal with ‘many’ concrete events. Thus the abstract concept ‘chair’ can cover many different concrete chairs which have only a few properties in common. Additionally the chairs can occur in different ‘contexts’ associating them with different ‘relations’ which can specify possible different ‘usages’ of the concept ‘chair’.
Thus, if the actor perceives something which ‘matches’ some ‘known’ concept then the actor is not only conscious about the empirical concrete phenomenon but also simultaneously about the abstract concept which will automatically be activated. ‘Immediately’ the actor ‘knows’ that this empirical something is e.g. a ‘chair’. Concrete: this concrete something is matching an abstract concept ‘chair’ which can as such cover many other concrete things too which can be as concrete somethings partially different from another concrete something.
From this follows an interesting side effect: while an actor can easily decide, whether a concrete something is there (“it is the case, that” = “it is true”) or not (“it is not the case, that” = “it isnot true” = “it is false”), an actor can not directly decide whether an abstract concept like ‘chair’ as such is ‘true’ in the sense, that the concept ‘as a whole’ corresponds to concrete empirical occurrences. This depends from the fact that an abstract concept like ‘chair’ can match with a nearly infinite set of possible concrete somethings which are called ‘possible instances’ of the abstract concept. But a human actor can directly ‘check’ only a ‘few’ concrete somethings. Therefore the usage of abstract concepts like ‘chair’, ‘house’, ‘bottle’ etc. implies inherently an ‘open set’ of ‘possible’ concrete exemplars and therefor is the usage of such concepts necessarily a ‘hypothetical’ usage. Because we can ‘in principle’ check the real extensions of these abstract concepts in everyday life as long there is the ‘freedom’ to do such checks, we are losing the ‘truth’ of our concepts and thereby the basis for a realistic cooperation, if this ‘freedom of checking’ is not possible.
If some incoming perception is ‘not yet known’, because nothing given in the unconsciousness does ‘match’, it is in a basic sens ‘new’ and the brain will automatically generate a ‘new concept’.
THE DIMENSION OF MEANING
In Figure 2 one can find two other components: the ‘meaning relation’ which maps concepts into ‘language expression’.
Language expressions inside the brain correspond to a diversity of visual, auditory, tactile or other empirical event sequences, which are in use for communicative acts.
These language expressions are usually not ‘isolated structures’ but are embedded in relations which map the expression structures to conceptual structures including the different substantiations of the abstract concepts and the associated contexts. By these relations the expressions are attached to the conceptual structures which are called the ‘meaning‘ of the expressions and vice versa the expressions are called the ‘language articulation’ of the meaning structures.
As far as conceptual structures are related via meaning relations to language expressions then a perception can automatically cause the ‘activation’ of the associated language expressions, which in turn can be uttered in some way. But conceptual structures can exist (especially with children) without an available meaning relation.
When language expressions are used within a communicative act then their usage can activate in all participants of the communication the ‘learned’ concepts as their intended meanings. Heaving the meaning activated in someones ‘consciousness’ this is a real phenomenon for that actor. But from the occurrence of concepts alone does not automatically follow, that a concept is ‘backed up’ by some ‘real matter’ in the external world. Someone can utter that it is raining, in the hearer of this utterance the intended concepts can become activated, but in the outside external world no rain is happening. In this case one has to state that the utterance of the language expressions “Look, its raining” has no counterpart in the real world, therefore we call the utterance in this case ‘false‘ or ‘not true‘.
THE DIMENSION OF TIME
The preceding figure 2 of the conceptual space is not yet complete. There is another important dimension based on the ability of the unconscious brain to ‘store’ certain structures in a ‘timely order’ which enables an actor — under certain conditions ! — to decide whether a certain structure X occurred in the consciousness ‘before’ or ‘after’ or ‘at the same time’ as another structure Y.
Evidently the unconscious brain is able do exactly this: (i) it can arrange the different structures under certain conditions in a ‘timely order’; (ii) it can detect ‘differences‘ between timely succeeding structures; the brain (iii) can conceptualize these changes as ‘change concepts‘ (‘rules of change’), and it can can classify different kinds of change like ‘deterministic’, ‘non-deterministic’ with different kinds of probabilities, as well as ‘arbitrary’ as in the case of ‘free learning systems‘. Free learning systems are able to behave in a ‘deterministic-like manner’, but they can also change their patterns on account of internal learning and decision processes in nearly any direction.
Based on memories of conceptual structures and derived change concepts (rules of change) the unconscious brain is able to generate different kinds of ‘possible configurations’, whose quality is depending from the degree of dependencies within the ‘generating criteria’: (i) no special restrictions; (ii) empirical restrictions; (iii) empirical restrictions for ‘upcoming states’ (if all drinkable water would be consumed, then one cannot plan any further with drinkable water).