Category Archives: intelligent machine

Homo Sapiens: empirical and sustained-empirical theories, emotions, and machines. A sketch

Author: Gerd Doeben-Henisch

Email: info@uffmm.org

Aug 24, 2023 — Aug 29, 2023 (10:48h CET)

Attention: This text has been translated from a German source by using the software deepL for nearly 97 – 99% of the text! The diagrams of the German version have been left out.

CONTEXT

This text represents the outline of a talk given at the conference “AI – Text and Validity. How do AI text generators change scientific discourse?” (August 25/26, 2023, TU Darmstadt). [1] A publication of all lectures is planned by the publisher Walter de Gruyter by the end of 2023/beginning of 2024. This publication will be announced here then.

Start of the Lecture

Dear Auditorium,

This conference entitled “AI – Text and Validity. How do AI text generators change scientific discourses?” is centrally devoted to scientific discourses and the possible influence of AI text generators on these. However, the hot core ultimately remains the phenomenon of text itself, its validity.

In this conference many different views are presented that are possible on this topic.

TRANSDISCIPLINARY

My contribution to the topic tries to define the role of the so-called AI text generators by embedding the properties of ‘AI text generators’ in a ‘structural conceptual framework’ within a ‘transdisciplinary view’. This helps the specifics of scientific discourses to be highlighted. This can then result further in better ‘criteria for an extended assessment’ of AI text generators in their role for scientific discourses.

An additional aspect is the question of the structure of ‘collective intelligence’ using humans as an example, and how this can possibly unite with an ‘artificial intelligence’ in the context of scientific discourses.

‘Transdisciplinary’ in this context means to span a ‘meta-level’ from which it should be possible to describe today’s ‘diversity of text productions’ in a way that is expressive enough to distinguish ‘AI-based’ text production from ‘human’ text production.

HUMAN TEXT GENERATION

The formulation ‘scientific discourse’ is a special case of the more general concept ‘human text generation’.

This change of perspective is meta-theoretically necessary, since at first sight it is not the ‘text as such’ that decides about ‘validity and non-validity’, but the ‘actors’ who ‘produce and understand texts’. And with the occurrence of ‘different kinds of actors’ – here ‘humans’, there ‘machines’ – one cannot avoid addressing exactly those differences – if there are any – that play a weighty role in the ‘validity of texts’.

TEXT CAPABLE MACHINES

With the distinction in two different kinds of actors – here ‘humans’, there ‘machines’ – a first ‘fundamental asymmetry’ immediately strikes the eye: so-called ‘AI text generators’ are entities that have been ‘invented’ and ‘built’ by humans, it are furthermore humans who ‘use’ them, and the essential material used by so-called AI generators are again ‘texts’ that are considered a ‘human cultural property’.

In the case of so-called ‘AI-text-generators’, we shall first state only this much, that we are dealing with ‘machines’, which have ‘input’ and ‘output’, plus a minimal ‘learning ability’, and whose input and output can process ‘text-like objects’.

BIOLOGICAL — NON-BIOLOGICAL

On the meta-level, then, we are assumed to have, on the one hand, such actors which are minimally ‘text-capable machines’ – completely human products – and, on the other hand, actors we call ‘humans’. Humans, as a ‘homo-sapiens population’, belong to the set of ‘biological systems’, while ‘text-capable machines’ belong to the set of ‘non-biological systems’.

BLANK INTELLIGENCE TERM

The transformation of the term ‘AI text generator’ into the term ‘text capable machine’ undertaken here is intended to additionally illustrate that the widespread use of the term ‘AI’ for ‘artificial intelligence’ is rather misleading. So far, there exists today no general concept of ‘intelligence’ in any scientific discipline that can be applied and accepted beyond individual disciplines. There is no real justification for the almost inflationary use of the term AI today other than that the term has been so drained of meaning that it can be used anytime, anywhere, without saying anything wrong. Something that has no meaning can be neither true’ nor ‘false’.

PREREQUISITES FOR TEXT GENERATION

If now the homo-sapiens population is identified as the original actor for ‘text generation’ and ‘text comprehension’, it shall now first be examined which are ‘those special characteristics’ that enable a homo-sapiens population to generate and comprehend texts and to ‘use them successfully in the everyday life process’.

VALIDITY

A connecting point for the investigation of the special characteristics of a homo-sapiens text generation and a text understanding is the term ‘validity’, which occurs in the conference topic.

In the primary arena of biological life, in everyday processes, in everyday life, the ‘validity’ of a text has to do with ‘being correct’, being ‘appicable’. If a text is not planned from the beginning with a ‘fictional character’, but with a ‘reference to everyday events’, which everyone can ‘check’ in the context of his ‘perception of the world’, then ‘validity in everyday life’ has to do with the fact that the ‘correctness of a text’ can be checked. If the ‘statement of a text’ is ‘applicable’ in everyday life, if it is ‘correct’, then one also says that this statement is ‘valid’, one grants it ‘validity’, one also calls it ‘true’. Against this background, one might be inclined to continue and say: ‘If’ the statement of a text ‘does not apply’, then it has ‘no validity’; simplified to the formulation that the statement is ‘not true’ or simply ‘false’.

In ‘real everyday life’, however, the world is rarely ‘black’ and ‘white’: it is not uncommon that we are confronted with texts to which we are inclined to ascribe ‘a possible validity’ because of their ‘learned meaning’, although it may not be at all clear whether there is – or will be – a situation in everyday life in which the statement of the text actually applies. In such a case, the validity would then be ‘indeterminate’; the statement would be ‘neither true nor false’.

ASYMMETRY: APPLICABLE- NOT APPLICABLE

One can recognize a certain asymmetry here: The ‘applicability’ of a statement, its actual validity, is comparatively clear. The ‘not being applicable’, i.e. a ‘merely possible’ validity, on the other hand, is difficult to decide.

With this phenomenon of the ‘current non-decidability’ of a statement we touch both the problem of the ‘meaning’ of a statement — how far is at all clear what is meant? — as well as the problem of the ‘unfinishedness of our everyday life’, better known as ‘future’: whether a ‘current present’ continues as such, whether exactly like this, or whether completely different, depends on how we understand and estimate ‘future’ in general; what some take for granted as a possible future, can be simply ‘nonsense’ for others.

MEANING

This tension between ‘currently decidable’ and ‘currently not yet decidable’ additionally clarifies an ‘autonomous’ aspect of the phenomenon of meaning: if a certain knowledge has been formed in the brain and has been made usable as ‘meaning’ for a ‘language system’, then this ‘associated’ meaning gains its own ‘reality’ for the scope of knowledge: it is not the ‘reality beyond the brain’, but the ‘reality of one’s own thinking’, whereby this reality of thinking ‘seen from outside’ has something like ‘being virtual’.

If one wants to talk about this ‘special reality of meaning’ in the context of the ‘whole system’, then one has to resort to far-reaching assumptions in order to be able to install a ‘conceptual framework’ on the meta-level which is able to sufficiently describe the structure and function of meaning. For this, the following components are minimally assumed (‘knowledge’, ‘language’ as well as ‘meaning relation’):

KNOWLEDGE: There is the totality of ‘knowledge’ that ‘builds up’ in the homo-sapiens actor in the course of time in the brain: both due to continuous interactions of the ‘brain’ with the ‘environment of the body’, as well as due to interactions ‘with the body itself’, as well as due to interactions ‘of the brain with itself’.

LANGUAGE: To be distinguished from knowledge is the dynamic system of ‘potential means of expression’, here simplistically called ‘language’, which can unfold over time in interaction with ‘knowledge’.

MEANING RELATIONSHIP: Finally, there is the dynamic ‘meaning relation’, an interaction mechanism that can link any knowledge elements to any language means of expression at any time.

Each of these mentioned components ‘knowledge’, ‘language’ as well as ‘meaning relation’ is extremely complex; no less complex is their interaction.

FUTURE AND EMOTIONS

In addition to the phenomenon of meaning, it also became apparent in the phenomenon of being applicable that the decision of being applicable also depends on an ‘available everyday situation’ in which a current correspondence can be ‘concretely shown’ or not.

If, in addition to a ‘conceivable meaning’ in the mind, we do not currently have any everyday situation that sufficiently corresponds to this meaning in the mind, then there are always two possibilities: We can give the ‘status of a possible future’ to this imagined construct despite the lack of reality reference, or not.

If we would decide to assign the status of a possible future to a ‘meaning in the head’, then there arise usually two requirements: (i) Can it be made sufficiently plausible in the light of the available knowledge that the ‘imagined possible situation’ can be ‘transformed into a new real situation’ in the ‘foreseeable future’ starting from the current real situation? And (ii) Are there ‘sustainable reasons’ why one should ‘want and affirm’ this possible future?

The first requirement calls for a powerful ‘science’ that sheds light on whether it can work at all. The second demand goes beyond this and brings the seemingly ‘irrational’ aspect of ’emotionality’ into play under the garb of ‘sustainability’: it is not simply about ‘knowledge as such’, it is also not only about a ‘so-called sustainable knowledge’ that is supposed to contribute to supporting the survival of life on planet Earth — and beyond –, it is rather also about ‘finding something good, affirming something, and then also wanting to decide it’. These last aspects are so far rather located beyond ‘rationality’; they are assigned to the diffuse area of ’emotions’; which is strange, since any form of ‘usual rationality’ is exactly based on these ’emotions’.[2]

SCIENTIFIC DISCOURSE AND EVERYDAY SITUATIONS

In the context of ‘rationality’ and ’emotionality’ just indicated, it is not uninteresting that in the conference topic ‘scientific discourse’ is thematized as a point of reference to clarify the status of text-capable machines.

The question is to what extent a ‘scientific discourse’ can serve as a reference point for a successful text at all?

For this purpose it can help to be aware of the fact that life on this planet earth takes place at every moment in an inconceivably large amount of ‘everyday situations’, which all take place simultaneously. Each ‘everyday situation’ represents a ‘present’ for the actors. And in the heads of the actors there is an individually different knowledge about how a present ‘can change’ or will change in a possible future.

This ‘knowledge in the heads’ of the actors involved can generally be ‘transformed into texts’ which in different ways ‘linguistically represent’ some of the aspects of everyday life.

The crucial point is that it is not enough for everyone to produce a text ‘for himself’ alone, quite ‘individually’, but that everyone must produce a ‘common text’ together ‘with everyone else’ who is also affected by the everyday situation. A ‘collective’ performance is required.

Nor is it a question of ‘any’ text, but one that is such that it allows for the ‘generation of possible continuations in the future’, that is, what is traditionally expected of a ‘scientific text’.

From the extensive discussion — since the times of Aristotle — of what ‘scientific’ should mean, what a ‘theory’ is, what an ’empirical theory’ should be, I sketch what I call here the ‘minimal concept of an empirical theory’.

  1. The starting point is a ‘group of people’ (the ‘authors’) who want to create a ‘common text’.
  2. This text is supposed to have the property that it allows ‘justifiable predictions’ for possible ‘future situations’, to which then ‘sometime’ in the future a ‘validity can be assigned’.
  3. The authors are able to agree on a ‘starting situation’ which they transform by means of a ‘common language’ into a ‘source text’ [A].
  4. It is agreed that this initial text may contain only ‘such linguistic expressions’ which can be shown to be ‘true’ ‘in the initial situation’.
  5. In another text, the authors compile a set of ‘rules of change’ [V] that put into words ‘forms of change’ for a given situation.
  6. Also in this case it is considered as agreed that only ‘such rules of change’ may be written down, of which all authors know that they have proved to be ‘true’ in ‘preceding everyday situations’.
  7. The text with the rules of change V is on a ‘meta-level’ compared to the text A about the initial situation, which is on an ‘object-level’ relative to the text V.
  8. The ‘interaction’ between the text V with the change rules and the text A with the initial situation is described in a separate ‘application text’ [F]: Here it is described when and how one may apply a change rule (in V) to a source text A and how this changes the ‘source text A’ to a ‘subsequent text A*’.
  9. The application text F is thus on a next higher meta-level to the two texts A and V and can cause the application text to change the source text A.
  1. The moment a new subsequent text A* exists, the subsequent text A* becomes the new initial text A.
  2. If the new initial text A is such that a change rule from V can be applied again, then the generation of a new subsequent text A* is repeated.
  3. This ‘repeatability’ of the application can lead to the generation of many subsequent texts <A*1, …, A*n>.
  4. A series of many subsequent texts <A*1, …, A*n> is usually called a ‘simulation’.
  5. Depending on the nature of the source text A and the nature of the change rules in V, it may be that possible simulations ‘can go quite differently’. The set of possible scientific simulations thus represents ‘future’ not as a single, definite course, but as an ‘arbitrarily large set of possible courses’.
  6. The factors on which different courses depend are manifold. One factor are the authors themselves. Every author is, after all, with his corporeality completely himself part of that very empirical world which is to be described in a scientific theory. And, as is well known, any human actor can change his mind at any moment. He can literally in the next moment do exactly the opposite of what he thought before. And thus the world is already no longer the same as previously assumed in the scientific description.

Even this simple example shows that the emotionality of ‘finding good, wanting, and deciding’ lies ahead of the rationality of scientific theories. This continues in the so-called ‘sustainability discussion’.

SUSTAINABLE EMPIRICAL THEORY

With the ‘minimal concept of an empirical theory (ET)’ just introduced, a ‘minimal concept of a sustainable empirical theory (NET)’ can also be introduced directly.

While an empirical theory can span an arbitrarily large space of grounded simulations that make visible the space of many possible futures, everyday actors are left with the question of what they want to have as ‘their future’ out of all this? In the present we experience the situation that mankind gives the impression that it agrees to destroy the life beyond the human population more and more sustainably with the expected effect of ‘self-destruction’.

However, this self-destruction effect, which can be predicted in outline, is only one variant in the space of possible futures. Empirical science can indicate it in outline. To distinguish this variant before others, to accept it as ‘good’, to ‘want’ it, to ‘decide’ for this variant, lies in that so far hardly explored area of emotionality as root of all rationality.[2]

If everyday actors have decided in favor of a certain rationally lightened variant of possible future, then they can evaluate at any time with a suitable ‘evaluation procedure (EVAL)’ how much ‘percent (%) of the properties of the target state Z’ have been achieved so far, provided that the favored target state is transformed into a suitable text Z.

In other words, the moment we have transformed everyday scenarios into a rationally tangible state via suitable texts, things take on a certain clarity and thereby become — in a sense — simple. That we make such transformations and on which aspects of a real or possible state we then focus is, however, antecedent to text-based rationality as an emotional dimension.[2]

MAN-MACHINE

After these preliminary considerations, the final question is whether and how the main question of this conference, “How do AI text generators change scientific discourse?” can be answered in any way?

My previous remarks have attempted to show what it means for humans to collectively generate texts that meet the criteria for scientific discourse that also meets the requirements for empirical or even sustained empirical theories.

In doing so, it becomes apparent that both in the generation of a collective scientific text and in its application in everyday life, a close interrelation with both the shared experiential world and the dynamic knowledge and meaning components in each actor play a role.

The aspect of ‘validity’ is part of a dynamic world reference whose assessment as ‘true’ is constantly in flux; while one actor may tend to say “Yes, can be true”, another actor may just tend to the opposite. While some may tend to favor possible future option X, others may prefer future option Y. Rational arguments are absent; emotions speak. While one group has just decided to ‘believe’ and ‘implement’ plan Z, the others turn away, reject plan Z, and do something completely different.

This unsteady, uncertain character of future-interpretation and future-action accompanies the Homo Sapiens population from the very beginning. The not understood emotional complex constantly accompanies everyday life like a shadow.

Where and how can ‘text-enabled machines’ make a constructive contribution in this situation?

Assuming that there is a source text A, a change text V and an instruction F, today’s algorithms could calculate all possible simulations faster than humans could.

Assuming that there is also a target text Z, today’s algorithms could also compute an evaluation of the relationship between a current situation as A and the target text Z.

In other words: if an empirical or a sustainable-empirical theory would be formulated with its necessary texts, then a present algorithm could automatically compute all possible simulations and the degree of target fulfillment faster than any human alone.

But what about the (i) elaboration of a theory or (ii) the pre-rational decision for a certain empirical or even sustainable-empirical theory ?

A clear answer to both questions seems hardly possible to me at the present time, since we humans still understand too little how we ourselves collectively form, select, check, compare and also reject theories in everyday life.

My working hypothesis on the subject is: that we will very well need machines capable of learning in order to be able to fulfill the task of developing useful sustainable empirical theories for our common everyday life in the future. But when this will happen in reality and to what extent seems largely unclear to me at this point in time.[2]

COMMENTS

[1] https://zevedi.de/en/topics/ki-text-2/

[2] Talking about ’emotions’ in the sense of ‘factors in us’ that move us to go from the state ‘before the text’ to the state ‘written text’, that hints at very many aspects. In a small exploratory text “State Change from Non-Writing to Writing. Working with chatGPT4 in parallel” ( https://www.uffmm.org/2023/08/28/state-change-from-non-writing-to-writing-working-with-chatgpt4-in-parallel/ ) the author has tried to address some of these aspects. While writing it becomes clear that very many ‘individually subjective’ aspects play a role here, which of course do not appear ‘isolated’, but always flash up a reference to concrete contexts, which are linked to the topic. Nevertheless, it is not the ‘objective context’ that forms the core statement, but the ‘individually subjective’ component that appears in the process of ‘putting into words’. This individual subjective component is tentatively used here as a criterion for ‘authentic texts’ in comparison to ‘automated texts’ like those that can be generated by all kinds of bots. In order to make this difference more tangible, the author decided to create an ‘automated text’ with the same topic at the same time as the quoted authentic text. For this purpose he used chatGBT4 from openAI. This is the beginning of a philosophical-literary experiment, perhaps to make the possible difference more visible in this way. For purely theoretical reasons, it is clear that a text generated by chatGBT4 can never generate ‘authentic texts’ in origin, unless it uses as a template an authentic text that it can modify. But then this is a clear ‘fake document’. To prevent such an abuse, the author writes the authentic text first and then asks chatGBT4 to write something about the given topic without chatGBT4 knowing the authentic text, because it has not yet found its way into the database of chatGBT4 via the Internet.

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.

 

 

INTELLIGENT MACHINES – INTRODUCTION

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

 Remark April 2022

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

OVERVIEW

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

IM WITHIN AAI

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

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

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

This has to be explained with some more details.

An Intelligent Machine (IM) in an Actor Story

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

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

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

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

Learning System

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

Def: Learning System (LS)

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

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

Intelligent System

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Measuring Intelligence by Actor Stories

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

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

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

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

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

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

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

Def: Combined Task-Fields (TF)

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

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

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

Measurement Comments

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

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

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

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

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

COMMENTS

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

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

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

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

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

uffmm – RESTART AS SCIENTIFIC WORKPLACE

RESTART OF UFFMM AS SCIENTIFIC WORKPLACE.
For the Integrated Engineering of the Future (SW4IEF)
Campaining the Actor-Actor Systems Engineering (AASE) paradigm

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

Last Update June-22, 2018, 15:32 CET.  See below: Case Studies —  Templates – AASE Micro Edition – and Scheduling 2018 —

RESTART

This is a complete new restart of the old uffmm-site. It is intended as a working place for those people who are interested in an integrated engineering of the future.

SYSTEMS ENGINEERING

A widely known and useful concept for a general approach to the engineering of problems is systems engineering (SE).

Open for nearly every kind of a possible problem does a systems engineering process (SEP) organize the process how to analyze the problem, and turn this analysis into a possible design for a solution. This proposed solution will be examined by important criteria and, if it reaches an optimal version, it will be implemented as a real working system. After final evaluations this solution will start its carrier in the real world.

PHILOSOPHY OF SCIENCE

In a meta-scientific point of view the systems engineering process can become itself the object of an analysis. This is usually done by a discipline called philosophy of science (PoS). Philosophy of science is asking, e.g., what the ‘ingredients’ of an systems-engineering process are, or how these ingredients do interact? How can such a process ‘fail’? ‘How can such a process be optimized’? Therefore a philosophy of science perspective can help to make a systems engineering process more transparent and thereby supports an optimization of these processes.

AAI (KNOWN AS HMI, HCI …)

A core idea of the philosophy of science perspective followed in this text is the assumption, that a systems engineering process is primarily based on different kinds of actors (AC) whose interactions enable and direct the whole process. These assumptions are also valid in that case, where the actors are not any more only biological systems like human persons and non-biological systems called machines, but also in that case where the traditional machines (M) are increasingly replaced by ‘intelligent machines (IM)‘. Therefore the well know paradigm of human-machine interaction (HMI) — or earlier ‘human-computer interaction (HCI)’  will be replaced in this text by the new paradigm of Actor-Actor Interaction (AAI). In this new version the main perspective is not the difference of man on one side and machines on the other but the kind of interactions between actors of all kind which are necessary and possible.

INTELLIGENT MACHINES

The  concept of intelligent machines (IM) is understood here as a special case of the general Actor (A) concept which includes as other sub-cases biological systems, predominantly humans as instantiations of the species Homo Sapiens. While until today the question of biological intelligence and machine intelligence is usually treated separately and differently it is intended in this text to use one general concept of intelligence for all actors. This allows then more direct comparisons and evaluations. Whether biological actors are in some sense better than the non-biological actors or vice versa can seriously only be discussed when the used concept of intelligence is the same.

ACTOR STORY AND ACTOR MODELS

And, as it will be explained in the following sections, the used paradigm of actor-actor interactions uses the two main concepts of actor story (AS) as well as actor model (AM). Actor models are embedded in the actor stories. Whether an actor model describes biological or non-biological actors does not matter. Independent of the inner structures of an actor model (which can be completely different) the actor story is always  completely described in terms of observable behavior which are the same for all kinds of actors (Comment: The major scientific disciplines for the analysis of behavior are biology, psychology, and sociology).

AASE PARADIGM

In analogy to the so-called ‘Object-Oriented (OO) approach in Software-Engineering (SWE)’ we campaign here the ‘Actor-Actor (AA) Systems Engineering (SE)’ approach. This takes the systems Engineering approach as a base concepts and re-works the whole framework from the point of view of the actor-actor paradigm.  AASE is seen here as a theory as well as an   domain of applications.

Ontologies of the AASE paradigm
Figure: Ontologies of the AASE paradigm

To understand the different perspectives of the used theory it can help to the figure ‘AASE-Paradigm Ontologies’. Within the systems engineering process (SEP) we have AAI-experts as acting actors. To describe these we need a ‘meta-level’ realized by a ‘philosophy of the actor’. The AAI-experts themselves are elaborating within an AAI-analysis an actor story (AS) as framework for different kinds of intended actors. To describe the inner structures of these intended actors one needs different kinds of ‘actor models’. The domain of actor-model structures overlaps with the domain of ‘machine learning (ML)’ and with ‘artificial intelligence (AI)’.

SOFTWARE

What will be described and developed separated from these theoretical considerations is an appropriate software environment which allows the construction of solutions within the AASE approach including e.g. the construction of intelligent machines too. This software environment is called in this text emerging-mind lab (EML) and it will be another public blog as well.

 

THEORY MICRO EDITION & CASE STUDIES

How we proceed

Because the overall framework of the intended integrated theory is too large to write it down in one condensed text with  all the necessary illustrating examples we decided in Dec 2017 to follow a bottom-up approach by writing primarily case studies from different fields. While doing this we can introduce stepwise the general theory by developing a Micro Edition of the Theory in parallel to the case studies. Because the Theory Micro Edition has gained a sufficient minimal completeness already in April 2018 we do not need anymore a separate   template for case studies. We will use the Theory Micro Edition  as  ‘template’ instead.

To keep the case studies readable as far as possible all needed mathematical concepts and formulas will be explained in a separate appendix section which is central for all case studies. This allows an evolutionary increase in the formal apparatus used for the integrated theory.

THEORY IN A BOOK FORMAT

(Still not final)

Here you can find the actual version of the   theory which will continuously be updated and extended by related topics.

At the end of the text you find a list of ToDos where everybody is invited to collaborate. The main editor is Gerd Doeben-Henisch deciding whether the proposal fits into the final text or not.

Last Update 22.June 2018

Philosophy of the Actor

This sections describes basic assumptions about the cognitive structure of the human AAI expert.

From HCI to AAI. Some Bits of History

This sections describes main developments in the history from HCI to AAI.

SCHEDULE 2018

The Milestone for a first outline in a book format has been reached June-22, 2018. The   milestone for a first final version   is  scheduled   for October-4, 2018.