AAI-THEORY V2 – BLUEPRINT: Bottom-up

eJournal: uffmm.org,
ISSN 2567-6458, 27.February 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

Last change: 28.February 2019 (Several corrections)

CONTEXT

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
Outine of the process how to generate an AS
Figure 1: Outline of the process how to generate an AS with a bottom-up approach

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
Epistemology of concepts used in an AAI Analysis rprocess
Fig.2: Epistemology of concepts used in an AAI Analysis process

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 dimension of time based on past experience and combinatoric thinking
Fig.3: The dimension of time based on past experience and combinatoric thinking

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).

 

 

 

 

 

 

 

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.

 

 

 

 

 

 

 

 

ACTOR-ACTOR INTERACTION ANALYSIS – A rough Outline of the Blueprint

eJournal: uffmm.org,
ISSN 2567-6458, 13.February 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

Last corrections: 14.February 2019 (add some more keywords; added  emphasizes for central words)

Change: 5.May 2019 (adding the the aspect of simulation and gaming; extending the view of the driving actors)

CONTEXT

An overview to the enhanced AAI theory  version 2 you can find here.  In this post we talk about the blueprint  of the whole  AAI analysis process. Here I leave out the topic of actor models (AM); the aspect of  simulation and gaming is mentioned only shortly. For these topics see other posts.

THE AAI ANALYSIS BLUEPRINT

Blueprint of the whole AAI analysis process including the epistemological assumptions. Not shown here is the whole topic of actor models (AM) and as well simulation.
Blueprint of the whole AAI analysis process including the epistemological assumptions. Not shown here is the whole topic of actor models (AM) and as well simulation.

The Actor-Actor Interaction (AAI) analysis is understood here as part of an  embracing  systems engineering process (SEP), which starts with the statement of a problem (P) which includes a vision (V) of an improved alternative situation. It has then to be analyzed how such a new improved situation S+ looks like; how one can realize certain tasks (T)  in an improved way.

DRIVING ACTORS

The driving actors for such an AAI analysis are at least one  stakeholder (STH) which communicates a problem P and an envisioned solution (ES) to an  expert (EXPaai) with a sufficient AAI experience. This expert will take   the lead in the process of transforming the problem and the envisioned  solution into a working solution (WS).

In the classical industrial case the stakeholder can be a group of managers from some company and the expert is also represented by a whole team of experts from different disciplines, including the AAI perspective as leading perspective.

In another case which  I will call here the  communal case — e.g. a whole city —      the stakeholder as well as the experts are members of the communal entity.   As   in the before mentioned cases there is some commonly accepted problem P combined  with a first envisioned solution ES, which shall be analyzed: what is needed to make it working? Can it work at all? What are costs? And many other questions can arise. The challenge to include all relevant experience and knowledge from all participants is at the center of the communication and to transform this available knowledge into some working solution which satisfies all stated requirements for all participants is a central  condition for the success of the project.

EPISTEMOLOGY

It has to be taken into account that the driving actors are able to do this job because they  have in their bodies brains (BRs) which in turn include  some consciousness (CNS). The processes and states beyond the consciousness are here called ‘unconscious‘ and the set of all these unconscious processes is called ‘the Unconsciousness’ (UCNS).

For more details to the cognitive processes see the post to the philosophical framework as well as the post bottom-up process. Both posts shall be integrated into one coherent view in the future.

SEMIOTIC SUBSYSTEM

An important set of substructures of the unconsciousness are those which enable symbolic language systems with so-called expressions (L) on one side and so-called non-expressions (~L) on the other. Embedded in a meaning relation (MNR) does the set of non-expressions ~L  function as the meaning (MEAN) of the expressions L, written as a mapping MNR: L <—> ~L. Depending from the involved sensors the expressions L can occur either as acoustic events L_spk, or as visual patterns written L_txt or visual patterns as pictures L_pict or even in other formats, which will not discussed here. The non-expressions can occur in every format which the brain can handle.

While written (symbolic) expressions L are only associated with the intended meaning through encoded mappings in the brain,  the spoken expressions L_spk as well as the pictorial ones L_pict can show some similarities with the intended meaning. Within acoustic  expressions one can ‘imitate‘ some sounds which are part of a meaning; even more can the pictorial expressions ‘imitate‘ the visual experience of the intended meaning to a high degree, but clearly not every kind of meaning.

DEFINING THE MAIN POINT OF REFERENCE

Because the space of possible problems and visions it nearly infinite large one has to define for a certain process the problem of the actual process together with the vision of a ‘better state of the affairs’. This is realized by a description of he problem in a problem document D_p as well as in a vision statement D_v. Because usually a vision is not without a given context one has to add all the constraints (C) which have to be taken into account for the possible solution.  Examples of constraints are ‘non-functional requirements’ (NFRs) like “safety” or “real time” or “without barriers” (for handicapped people). Part of the non-functional requirements are also definitions of win-lose states as part of a game.

AAI ANALYSIS – BASIC PROCEDURE

If the AAI check has been successful and there is at least one task T to be done in an assumed environment ENV and there are at least one executing actor A_exec in this task as well as an assisting actor A_ass then the AAI analysis can start.

ACTOR STORY (AS)

The main task is to elaborate a complete description of a process which includes a start state S* and a goal state S+, where  the participating executive actors A_exec can reach the goal state S+ by doing some actions. While the imagined process p_v  is a virtual (= cognitive/ mental) model of an intended real process p_e, this intended virtual model p_e can only be communicated by a symbolic expressions L embedded in a meaning relation. Thus the elaboration/ construction of the intended process will be realized by using appropriate expressions L embedded in a meaning relation. This can be understood as a basic mapping of sensor based perceptions of the supposed real world into some abstract virtual structures automatically (unconsciously) computed by the brain. A special kind of this mapping is the case of measurement.

In this text especially three types of symbolic expressions L will be used: (i) pictorial expressions L_pict, (ii) textual expressions of a natural language L_txt, and (iii) textual expressions of a mathematical language L_math. The meaning part of these symbolic expressions as well as the expressions itself will be called here an actor story (AS) with the different modes  pictorial AS (PAS), textual AS (TAS), as well as mathematical AS (MAS).

The basic elements of an  actor story (AS) are states which represent sets of facts. A fact is an expression of some defined language L which can be decided as being true in a real situation or not (the past and the future are special cases for such truth clarifications). Facts can be identified as actors which can act by their own. The transformation from one state to a follow up state has to be described with sets of change rules. The combination of states and change rules defines mathematically a directed graph (G).

Based on such a graph it is possible to derive an automaton (A) which can be used as a simulator. A simulator allows simulations. A concrete simulation takes a start state S0 as the actual state S* and computes with the aid of the change rules one follow up state S1. This follow up state becomes then the new actual state S*. Thus the simulation constitutes a continuous process which generally can be infinite. To make the simulation finite one has to define some stop criteria (C*). A simulation can be passive without any interruption or interactive. The interactive mode allows different external actors to select certain real values for the available variables of the actual state.

If in the problem definition certain win-lose states have been defined then one can turn an interactive simulation into a game where the external actors can try to manipulate the process in a way as to reach one of the defined win-states. As soon as someone (which can be a team) has reached a win-state the responsible actor (or team) has won. Such games can be repeated to allow accumulation of wins (or loses).

Gaming allows a far better experience of the advantages or disadvantages of some actor story as a rather lose simulation. Therefore the probability to detect aspects of an actor story with their given constraints is by gaming quite high and increases the probability to improve the whole concept.

Based on an actor story with a simulator it is possible to increase the cognitive power of exploring the future even more.  There exists the possibility to define an oracle algorithm as well as different kinds of intelligent algorithms to support the human actor further. This has to be described in other posts.

TAR AND AAR

If the actor story is completed (in a certain version v_i) then one can extract from the story the input-output profiles of every participating actor. This list represents the task-induced actor requirements (TAR).  If one is looking for concrete real persons for doing the job of an executing actor the TAR can be used as a benchmark for assessing candidates for this job. The profiles of the real persons are called here actor-actor induced requirements (AAR), that is the real profile compared with the ideal profile of the TAR. If the ‘distance’ between AAR and TAR is below some threshold then the candidate has either to be rejected or one can offer some training to improve his AAR; the other option is to  change the conditions of the TAR in a way that the TAR is more closer to the AARs.

The TAR is valid for the executive actors as well as for the assisting actors A_ass.

CONSTRAINTS CHECK

If the actor story has in some version V_i a certain completion one has to check whether the different constraints which accompany the vision document are satisfied through the story: AS_vi |- C.

Such an evaluation is only possible if the constraints can be interpreted with regard to the actor story AS in version vi in a way, that the constraints can be decided.

For many constraints it can happen that the constraints can not or not completely be decided on the level of the actor story but only in a later phase of the systems engineering process, when the actor story will be implemented in software and hardware.

MEASURING OF USABILITY

Using the actor story as a benchmark one can test the quality of the usability of the whole process by doing usability tests.

 

 

 

 

 

 

 

 

 

 

 

AAI THEORY V2 – AS –VIRTUAL MEANING AND AS

eJournal: uffmm.org,
ISSN 2567-6458, 29.Januar 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

Last Change: 1.February 2019 (Corrections)

— Outdated —

CONTEXT

An overview to the enhanced AAI theory  version 2 you can find here.  In this post we talk about the special topic of the virtual meaning and the actor story.

VIRTUAL MEANING AND ACTOR STORY

  1. In a textual actor story (TAS) using the symbolic expressions L0 of some everyday language one can describe a state with a finite set of facts which should be decidable ‘in principle’ when related to the supposed external empirical environment of the problem P. Thus the constraint of ‘operational decidability‘ with regard to an empirical external environment imposes some constraint of the kinds of symbolic expressions which can be used. If there is more than only one state (which is the usual case) then one has to provide a list of ‘possible changes‘. Each change is described with  a symbolic expression L0x. The content of a change is at least one fact which will change between the ‘given’ state and the ‘succeeding’ state. Thus the virtual meaning of an actor must enable the actor to distinguish between a ‘given state’ q_now  and a possible ‘succeeding state’ q_next. There can be more than one possible change with regard to a given state q_now. Thus a textual actor story (TAS) is a set of states connected by changes, all represented as finite collections of symbolic expressions.
  2. In a pictorial actor story (PAS) using the graphical  expressions Lg of some everyday pictorial langue one can describe a state with a finite set of facts realized as pictures of objects, properties as well as relations between these objects.  The graphs of the objects can be enhanced by graphs including symbolic expressions L0  of some everyday language.  Again it should be   decidable ‘in principle’ whether these pictorial facts can be  related to the suppose external empirical environment of the problem P. Thus the constraint of ‘operational decidability’ with regard to an empirical external environment imposes some constraint of the kinds of symbolic expressions which can be used. If there is more than only one state (which is the usual case) then one has to provide a list of ‘possible changes’. Each change is described with  an   expression Lgx. The content of a change is at least one fact which will change between the ‘given’ state and an ‘succeeding’ state. Thus the virtual meaning of an actor must enable the actor to distinguish between a ‘given state’ q_now  and a possible ‘succeeding state’ q_next. There can be more than one possible change with regard to a given state q_now. Thus a pictorial actor story (TAS) is a set of states connected by changes, all represented as finite collections of graphical  expressions.
  3. In the case of a mathematical actor story (MAS) one has to distinguish two cases: (i) a complete formal description or (ii) a graphical presentation enhanced with symbolic expressions.
  4. In case (i) it is similar to the textual mode but replacing the symbolic expressions L0 of  some everyday   langue with the symbolic expressions Lm of some mathematical language. In this book we are using predicate logic syntax with a new semantics. In case (ii) one describes the  actor story as a mathematical directed graph. The nodes (vertices) of the graph are understood as ‘states’ and the arrows connecting the nodes are  understood as changes. A node representing a state can be attached to a finite set   of facts, where a fact is a symbolic expression Lm  representing  objects, properties as well as relations between these objects.   Again it should be   decidable ‘in principle’ whether these facts  can be  related to the suppose external empirical environment of the problem P. Thus the constraint of ‘operational decidability’ with regard to an empirical external environment imposes some constraint of the kinds of symbolic expressions which can be used. If there is more than only one state (which is the usual case) then one has to use arrows which are labeled by symbolic change expressions Lmx.    The content of a change is at least one fact which will change between the ‘given’ state and an ‘succeeding’ state. Thus the virtual meaning of an actor must enable the actor to distinguish between a ‘given state’ q_now  and a possible ‘succeeding state’ q_next. There can be more than one possible change with regard to a given state q_now.
  5. If the complete actor story is given, then there is no need for the additional change expressions LX because one can infer the changes from the  pairs of the succeeding states directly. But if one wants to ‘generate’ an actor story beginning with the start state then one needs the list of change expressions.

AAI THEORY V2 – AS – MEANING: REAL AND VIRTUAL

eJournal: uffmm.org,
ISSN 2567-6458, 29.Januar 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

— Outdated —

CONTEXT

An overview to the enhanced AAI theory  version 2 you can find here.  In this post we talk about  the special topic of the meaning of (symbolic) expressions.

MEANING: REAL AND VIRTUAL

  1. In semiotic terminology  the ‘meaning‘ of a symbolic expression corresponds to the image of the mapping from symbolic expressions (L) into something else (non-L). This mapping is located in that system which is using this mapping. We can call this system a ‘semiotic system‘.
  2. For the generation of an actor story we assume that the AAI experts as well as all the other actors collaborating with the AAI actors are input-output systems with changeable internal states (IS) as well as a behavior function (phi), written as phi: I x IS —> IS x O.
  3. These actors are embedded in an empirical environment (ENV) which is continuously changing.
  4. Parts of the environment can interact with the actors by inducing physical state-changes in parts of the actors (Stimuli (S), Input, (I)) as well as receiving physical responses from the actors (Responses (R), output (O)) which change parts of the environmental states.
  5. Interpreting these actors as ‘semiotic systems’ implies that the actors can receive as input symbolic expressions (L) as well as non-symbolic events and they can output symbolic expressions (L) as well as some non-symbolic events (non-L). Furthermore the mapping from symbolic expressions into something else is assumed to happen ‘inside‘ the system.
  6. From a 3rd-person view one can distinguish the empirical environment external to the actor as well as the empirical states ‘inside’ the system (typically investigated by Physiology with many sub-disciplines).
  7. The internal states on the cellular level have a small subset called ‘brain’ (less than 1% of all cellular elements).  A  subset of the brain cells is enabling what in a 1st person view is called ‘consciousness‘.  The ‘content’ of the consciousness consists of ‘phenomena‘ which are not ’empirical’ in the usual sense of ’empirical’.  Using the consciousness as point of reference everything else of the actor which is not part of the conscious is ‘not conscious‘ or ‘unconscious‘. The ‘unconsciousness‘ is then the set of all non-conscious states of the actor (which means in the biological case of human sapiens more than 99% of all body states).
  8. As empirical sciences have revealed there exist functional relations between empirical states of the external environment (S_emp) and the set of externally caused internal  unconscious input states of the actor (IS_emp_uc).
  9. The internally caused unconscious input states (IS_emp_uc) are further processed and mapped in a variety of internal unconscious states (IS_emp_uc_abstr), which are more ‘general’ as the original input states. Thus subsets of internally cause unconscious  internal states IS_emp_uc  can be elements of the more abstract internal states IS_emp_uc_abstr.
  10. These internal unconscious states are part of ‘networks‘ and parts of different kinds of ‘hierarchies‘.
  11. There are many different kinds of internal operations working on these internal structures including the input states.
  12. Parts of the internal structures can function as ‘meaning‘ (M) for other parts of internal structures which function as ‘symbolic expressions‘ (L). Symbolic expressions together with the meaning constituting elements can be used from an actor (seen as a semiotic system) as a ‘symbolic language‘ whose observable part are the ‘symbols’ (written, spoken, gestures, …) and whose non-observable part is the mapping relation (encoding) from symbols into the internal meaning elements.
  13. The primary meaning of a language is therefore a ‘virtual world of states inside the actor‘ compared to the ‘external empirical world‘. Parts of the virtual meaning world can correspond to parts of the empirical world outside. To control such an important relationship one needs commonly defined empirical measurement procedures (MP) which are producing external empirical events which can be repeatedly perceived by a population of actors, which can compare these processes and events with their 1st person conscious phenomena (PH). If it is possible for an actor (an observer) to identify those phenomena which correspond to the external measurement events than it is possible (in principle) to define that subset of Phenomena (PH_emp) which are phenomena but are correlated with events in the external empirical world.  Usually those phenomena which correspond to empirical events external PH_emp are a true subset of the set of all possible Phenomena, written as PH_emp ⊂ PH.
  14. While the empirical phenomena PH_emp are ‘concrete‘ phenomena are the non-empirical phenomena PH_abs = PH-PH_emp ‘abstract‘ in the sense that an empirical phenomenon p_emp can be an element of a non-empirical phenomenon p_abs if p_emp is not new.
  15. While the virtual meaning of a symbolic language is realized by abstract structures which can be ‘cited’ in the consciousness as p_abs,  the empirical meaning   instead occurs as concrete structures which can be ‘cited’ by the consciousness.
  16. All meaning elements can occur as part of a virtual spatial structure (VR) and as part of a virtual timely structure (VT).
  17. There is no 1-to-1 mapping from the spatial and timely structures of the external empirical world into the virtual internal world of meanings.
  18. If it is possible to correlate virtual meaning structures with external empirical structures we call this ’empirical soundness’ or ’empirical truth’.

AAI THEORY V2 – Actor Story (AS)

eJournal: uffmm.org,
ISSN 2567-6458, 28.Januar 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

— Outdated —

CONTEXT

An overview to the enhanced AAI theory  version 2 you can find here.  In this post we talk about  the generation of the actor story (AS).

ACTOR STORY

To get from the problem P to an improved configuration S measured by some expectation  E needs a process characterized by a set of necessary states Q which are connected by necessary changes X. Such a process can be described with the aid of  an actor story AS.

  1. The target 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 X 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’, ‘Un1’, …) 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 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.
    2. 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). The drawings can be enhanced by fragments of texts.
    3. A mathematical mode generating a Mathematical Actor Story (MAS): this can be done either (i) by  a pictorial graph with nodes and edges as arrows associated with formal expressions or (ii)  by a complete formal structure without any pictorial elements.
    4. For every mode it has to be shown how an AAI expert can generate an actor story out of the virtual cognitive world of his brain and how it is possible to decide the empirical soundness of the actor story.

 

AAI THEORY V2 –EPISTEMOLOGY OF THE AAI-EXPERTS

eJournal: uffmm.org,
ISSN 2567-6458, 26.Januar 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

An overview to the enhanced AAI theory  version 2 you can find here.  In this post we talk about the fourth chapter dealing with the epistemology of actors within an AAI analysis process.

EPISTEMOLOGY AND THE EMPIRICAL SCIENCES

Epistemology is a sub-discipline of general philosophy. While a special discipline in empirical science is defined by a certain sub-set of the real world RW  by empirical measurement methods generating empirical data which can be interpreted by a formalized theory,  philosophy  is not restricted to a sub-field of the real world. This is important because an empirical discipline has no methods to define itself.  Chemistry e.g. can define by which kinds of measurement it is gaining empirical data   and it can offer different kinds of formal theories to interpret these data including inferences to forecast certain reactions given certain configurations of matters, but chemistry is not able  to explain the way how a chemist is thinking, how the language works which a chemist is using etc. Thus empirical science presupposes a general framework of bodies, sensors, brains, languages etc. to be able to do a very specialized  — but as such highly important — job. One can define ‘philosophy’ then as that kind of activity which tries to clarify all these  conditions which are necessary to do science as well as how cognition works in the general case.

Given this one can imagine that philosophy is in principle a nearly ‘infinite’ task. To get not lost in this conceptual infinity it is recommended to start with concrete processes of communications which are oriented to generate those kinds of texts which can be shown as ‘related to parts of the empirical world’ in a decidable way. This kind of texts   is here called ’empirically sound’ or ’empirically true’. It is to suppose that there will be texts for which it seems to be clear that they are empirically sound, others will appear ‘fuzzy’ for such a criterion, others even will appear without any direct relation to empirical soundness.

In empirical sciences one is using so-called empirical measurement procedures as benchmarks to decided whether one has empirical data or not, and it is commonly assumed that every ‘normal observer’ can use these data as every other ‘normal observer’. But because individual, single data have nearly no meaning on their own one needs relations, sets of relations (models) and even more complete theories, to integrate the data in a context, which allows some interpretation and some inferences for forecasting. But these relations, models, or theories can not directly be inferred from the real world. They have to be created by the observers as ‘working hypotheses’ which can fit with the data or not. And these constructions are grounded in  highly complex cognitive processes which follow their own built-in rules and which are mostly not conscious. ‘Cognitive processes’ in biological systems, especially in human person, are completely generated by a brain and constitute therefore a ‘virtual world’ on their own.  This cognitive virtual world  is not the result of a 1-to-1 mapping from the real world into the brain states.  This becomes important in that moment where the brain is mapping this virtual cognitive world into some symbolic language L. While the symbols of a language (sounds or written signs or …) as such have no meaning the brain enables a ‘coding’, a ‘mapping’ from symbolic expressions into different states of the brain. In the light’ of such encodings the symbolic expressions have some meaning.  Besides the fact that different observers can have different encodings it is always an open question whether the encoded meaning of the virtual cognitive space has something to do with some part of the empirical reality. Empirical data generated by empirical measurement procedures can help to coordinate the virtual cognitive states of different observers with each other, but this coordination is not an automatic process. Empirically sound language expressions are difficult to get and therefore of a high value for the survival of mankind. To generate empirically sound formal theories is even more demanding and until today there exists no commonly accepted concept of the right format of an empirically sound theory. In an era which calls itself  ‘scientific’ this is a very strange fact.

EPISTEMOLOGY OF THE AAI-EXPERTS

Applying these general considerations to the AAI experts trying to construct an actor story to describe at least one possible path from a start state to a goal state, one can pick up the different languages the AAI experts are using and asking back under which conditions these languages have some ‘meaning’ and under which   conditions these meanings can be called ’empirically sound’?

In this book three different ‘modes’ of an actor story will be distinguished:

  1. A textual mode using some ordinary everyday language, thus using spoken language (stored in an audio file) or written language as a text.
  2. A pictorial mode using a ‘language of pictures’, possibly enhanced by fragments of texts.
  3. A mathematical mode using graphical presentations of ‘graphs’ enhanced by symbolic expressions (text) and symbolic expressions only.

For every mode it has to be shown how an AAI expert can generate an actor story out of the virtual cognitive world of his brain and how it is possible to decided the empirical soundness of the actor story.

 

 

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.

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.

 

 

ACTOR-ACTOR INTERACTION. Philosophy of the Actor

eJournal: uffmm.org, ISSN 2567-6458
16.March 2018
Email: info@uffmm.org
Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de
Frankfurt University of Applied Sciences (FRA-UAS)
Institut for New Media (INM, Frankfurt)

PDF

CONTENTS

I   A Vision as a Problem to be Solved … 1
II   Language, Meaning & Ontology …  2
     II-A   Language Levels . . . . . . . . .  . . 2
     II-B  Common Empirical Matter .  . . . . . 2
     II-C   Perceptual Levels . . . . . . .  . . . . 3
     II-D   Space & Time . . . . . . . .  . . . . . 4
     II-E    Different Language Modes . . . 4
     II-F    Meaning of Expressions & Ontology … 4
     II-G   True Expressions . . . . . . .  . . . .  5
     II-H   The Congruence of Meaning  . . . .  5
III   Actor Algebra … 6
IV   World Algebra  … 7
V    How to continue … 8
VI References … 8

Abstract

As preparation for this text one should read the chapter about the basic layout of an Actor-Actor Analysis (AAA) as part of an systems engineering process (SEP). In this text it will be described which internal conditions one has to assume for an actor who uses a language to talk about his observations oft he world to someone else in a verifiable way. Topics which are explained in this text are e.g. ’language’,’meaning’, ’ontology’, ’consciousness’, ’true utterance’, ’synonymous expression.

AAI – Actor-Actor Interaction. A Philosophy of Science View

AAI – Actor-Actor Interaction.
A Philosophy of Science View
eJournal: uffmm.org, ISSN 2567-6458

Gerd Doeben-Henisch
info@uffmm.org
gerd@doeben-henisch.de

PDF

ABSTRACT

On the cover page of this blog you find a first general view on the subject matter of an integrated engineering approach for the future. Here we give a short description of the main idea of the analysis phase of systems engineering how this will be realized within the actor-actor interaction paradigm as described in this text.

INTRODUCTION

Overview of the analysis phase of systems engineering as realized within an actor-actor interaction paradigm
Overview of the analysis phase of systems engineering as realized within an actor-actor interaction paradigm

As you can see in figure Nr.1 there are the following main topics within the Actor-Actor Interaction (AAI) paradigm as used in this text (Comment: The more traditional formula is known as Human-Machine Interaction (HMI)):

Triggered by a problem document D_p from the problem phase (P) of the engineering process the AAI-experts have to analyze, what are the potential requirements following from this document, all the time also communicating with the stakeholder to keep in touch with the hidden intentions of the stakeholder.

The idea is to identify at least one task (T) with at least one goal state (G) which shall be arrived after running a task.

A task is assumed to represent a sequence of states (at least a start state and a goal state) which can have more than one option in every state, not excluding repetitions.

Every task presupposes some context (C) which gives the environment for the task.

The number of tasks and their length is in principle not limited, but their can be certain constraints (CS) given which have to be fulfilled required by the stakeholder or by some other important rules/ laws. Such constraints will probably limit the number of tasks as well as their length.

Actor Story

Every task as a sequence of states can be viewed as a story which describes a process. A story is a text (TXT) which is static and hides the implicit meaning in the brains of the participating actors. Only if an actor has some (learned) understanding of the used language then the actor is able to translate the perceptions of the process in an appropriate text and vice versa the text into corresponding perceptions or equivalently ‘thoughts’ representing the perceptions.

In this text it is assumed that a story is describing only the observable behavior of the participating actors, not their possible internal states (IS). For to describe the internal states (IS) it is further assumed that one describes the internal states in a new text called actor model (AM). The usual story is called an actor story (AS). Thus the actor story (AS) is the environment for the actor models (AM).

In this text three main modes of actor stories are distinguished:

  1. An actor story written in some everyday language L_0 called AS_L0 .
  2. A translation of the everyday language L_0 into a mathematical language L_math which can represent graphs, called AS_Lmath.
  3. A translation of the hidden meaning which resides in the brains of the AAI-experts into a pictorial language L_pict (like a comic strip), called AS_Lpict.

To make the relationship between the graph-version AS_Lmath and the pictorial version AS_Lpict visible one needs an explicit mapping Int from one version into the other one, like: Int : AS_Lmath <—> AS_Lpict. This mapping Int works like a lexicon from one language into another one.

From a philosophy of science point of view one has to consider that the different kinds of actor stories have a meaning which is rooted in the intended processes assumed to be necessary for the realization of the different tasks. The processes as such are dynamic, but the stories as such are static. Thus a stakeholder (SH) or an AAI-expert who wants to get some understanding of the intended processes has to rely on his internal brain simulations associated with the meaning of these stories. Because every actor has its own internal simulation which can not be perceived from the other actors there is some probability that the simulations of the different actors can be different. This can cause misunderstandings, errors, and frustrations.(Comment: This problem has been discussed in [DHW07])

One remedy to minimize such errors is the construction of automata (AT) derived from the math mode AS_Lmath of the actor stories. Because the math mode represents a graph one can derive Der from this version directly (and automatically) the description of an automaton which can completely simulate the actor story, thus one can assume Der(AS_Lmath) = AT_AS_Lmath.

But, from the point of view of Philosophy of science this derived automaton AT_AS_Lmath is still only a static text. This text describes the potential behavior of an automaton AT. Taking a real computer (COMP) one can feed this real computer with the description of the automaton AT AT_AS_Lmath and make the real computer behave like the described automaton. If we did this then we have a real simulation (SIM) of the theoretical behavior of the theoretical automaton AT realized by the real computer COMP. Thus we have SIM = COMP(AT_AS_Lmath). (Comment: These ideas have been discussed in [EDH11].)

Such a real simulation is dynamic and visible for everybody. All participating actors can see the same simulation and if there is some deviation from the intention of the stakeholder then this can become perceivable for everybody immediately.

Actor Model

As mentioned above the actor story (AS) describes only the observable behavior of some actor, but not possible internal states (IS) which could be responsible for the observable behavior.

If necessary it is possible to define for every actor an individual actor model; indeed one can define more than one model to explore the possibilities of different internal structures to enable a certain behavior.

The general pattern of actor models follows in this text the concept of input-output systems (IOSYS), which are in principle able to learn. What the term ‘learning’ designates concretely will be explained in later sections. The same holds of the term ‘intelligent’ and ‘intelligence’.

The basic assumptions about input-output systems used here reads a follows:

Def: Input-Output System (IOSYS)

IOSYS(x) iff x=< I, O, IS, phi>
phi : I x IS —> IS x O
I := Input
O := Output
IS := Internal

As in the case of the actor story (AS) the primary descriptions of actor models (AM) are static texts. To make the hidden meanings of these descriptions ‘explicit’, ‘visible’ one has again to convert the static texts into descriptions of automata, which can be feed into real computers which in turn then simulate the behavior of these theoretical automata as a real process.

Combining the real simulation of an actor story with the real simulations of all the participating actors described in the actor models can show a dynamic, impressive process which is full visible to all collaborating stakeholders and AAI-experts.

Testing

Having all actor stories and actor models at hand, ideally implemented as real simulations, one has to test the interaction of the elaborated actors with real actors, which are intended to work within these explorative stories and models. This is done by actor tests (former: usability tests) where (i) real actors are confronted with real tasks and have to perform in the intended way; (ii) real actors are interviewed with questionnaires about their subjective feelings during their task completion.

Every such test will yield some new insights how to change the settings a bit to gain eventually some improvements. Repeating these cycles of designing, testing, and modifying can generate a finite set of test-results T where possibly one subset is the ‘best’ compared to all the others. This can give some security that this design is probably the ‘relative best design’ with regards to T.

Further Readings:

  1. Analysis
  2. Simulation
  3. Testing
  4. User Modeling
  5. User Modeling and AI

For a newer version of the AAi-text see HERE..

REFERENCES

[DHW07] G. Doeben-Henisch and M. Wagner. Validation within safety critical systems engineering from a computation semiotics point of view.
Proceedings of the IEEE Africon2007 Conference, pages Pages: 1 – 7, 2007.
[EDH11] Louwrence Erasmus and Gerd Doeben-Henisch. A theory of the
system engineering process. In ISEM 2011 International Conference. IEEE, 2011.

EXAMPLE

For a toy-example to these concepts please see the post AAI – Actor-Actor Interaction. A Toy-Example, No.1