Category Archives: Actor-Actor Systems Engineering (AASE)

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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-Formalisms, Actor, Actor - human, Actor - non-human, actor-actor interaction (AAI), Actor-Actor Systems Engineering (AASE), communal planning, communication, context (in HMI), Future, HMI-expert, Human-Machine Interaction, infrastructure, Knowledge, Learning Environment, learning system (LS), public library, smart city, Systems Engineering, Systems Engineering Process

LIBRARIES AS ACTORS. WHAT ABOUT THE CITIZENS?

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

CONTEXT

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

PROBLEM

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

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

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

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

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

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

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

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

DISCUSSION

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

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

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

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

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

REFERENCES

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

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

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

AAI-expert, AAI-Formalisms, Actor - human, actor-actor interaction (AAI), Actor-Actor Systems Engineering (AASE), Basic concepts, Better-World-Project Idea, biological systems, communication, Domain of Reference, Engineering, Future, Global-Learning Space, Human-Machine Interaction, Learning Environment, Philosophy of Science, Philosophy of the actor, real simulation, Simulation, World Serious Games

THE BETTER WORLD PROJECT IDEA

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

Last changes: 9.Oct.2018 (Engineering part)

Author: Gerd Doeben-Henisch

Enhanced version of the 'Better World Project' Idea by making explicit the engineering part touching all other aspects
Enhanced version of the ‘Better World Project’ Idea by making explicit the engineering part touching all other aspects

 

The online-book project published on the uffmm.org website has to be seen within a bigger idea which can be named ‘The better world project’.

As outlined in the figure above you can see that the AAIwSE theory is the nucleus of a project which intends to enable a global learning space which connects individual persons as well as schools, universities, cities as well as companies, and even more if wanted.

There are other ideas around using the concept ‘better world’, butt these other concepts are targeting other subjects. In this view here the engineering perspective is laying the ground to build new more effective systems to enhance all aspects of life.

As you already can detect in the AAAIwSE theory published so far there exists a new and enlarged vision of the acting persons, the engineers as the great artists of the real world. Taking this view seriously there will be a need for a new kind of spirituality too which is enabling the acting persons to do all this with a vital interest in the future of life in the universe.

Actually the following websites are directly involved in the ‘Better World Project Idea’: this site ‘uffmm.org’ (in English)  and (in German)  ‘cognitiveagent.org‘ and ‘Kommunalpolitik & eGaming‘. The last link points to an official project of the Frankfurt University of Applied Sciences (FRA-UAS) which will apply the AAI-Methods to all communities in Germany (about 11.000).

AASE, Actor, actor - biological, Actor - human, Actor - non-human, actor model, actor-actor interaction (AAI), Actor-Actor Systems Engineering (AASE), actor-story (AS), analysis phase (in SE), Artificial Intelligence (AI), behavior function, biological systems, case study, constraints, environment, formal appendix, Formal Language, goal state, HCI history, HMI, input-output systems (IOSYS), Integrated engineering, intelligent, intelligent machine, intended user, interface, internal states (of systems), Knowledge, language - everyday L0, language - math Lmath, language - pictorial Lpict, learning, learning system (LS), Management of a Systems Engineering Process, math mode, meaning, non-biological systems, Philosophy of Science, Philosophy of the actor, Pictorial Actor Story, problem document, real computer, SEP, SEP-Manager, Simulation, Stakeholder, start state, system interface, Systems Engineering, Systems Engineering Process, task, Theory Micro Edition, Translation, usability test, User, User story, user story - as formal expression, user story - as graph, user story - pictorial, user story - simulation, user story - textual

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

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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

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CONTENTS

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

Abstract

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

 

 

AAI history, AASE, Actor, actor model, Actor-Actor Systems Engineering (AASE), Artificial Intelligence (AI), behavior function, biological systems, biology, case study, Emerging-Mind Lab, formal appendix, Future, Future-Building, HCI history, HMI history, Human-Machine Interaction, Integrated engineering, intelligent machine, Learning Environment, machine, Machine Friends, non-biological systems, observable behavior, Philosophy of Science, Philosophy of the actor, Psychoanalyst, psychology, sociology, Systems Engineering, Systems Engineering Process, Theory Micro Edition

uffmm – RESTART AS SCIENTIFIC WORKPLACE

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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.