Category Archives: gaming

HMI ANALYSIS, Part 4: Tool based Actor Story Development with Testing and Gaming

Integrating Engineering and the Human Factor (info@uffmm.org)
eJournal uffmm.org ISSN 2567-6458, March 3-4, 2021,
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

Last change: March 4, 2021, 07:49h (Minor corrections; relating to the UN SDGs)

HISTORY

As described in the uffmm eJournal  the wider context of this software project is an integrated  engineering theory called Distributed Actor-Actor Interaction [DAAI] further extended to the Collective Man-Machine Intelligence [CM:MI] paradigm.  This document is part of the Case Studies section.

HMI ANALYSIS, Part 4: Tool based Actor Story Development with Testing and Gaming

Context

This text is preceded by the following texts:

INFO GRAPH

Overview about different scenarios which will be possible for the development, simulation, testing and gaming of actor stories using the oksimo software tool

Introduction

In the preceding post it has been explained, how one can format an actor story [AS] as a theory in the  format  of  an Evaluated Theory Tε with Algorithmic Intelligence:   Tε,α=<M,∑,ε,α>.

In the following text it will be explained which kinds of different scenarios will be possible to elaborate, to simulate, to test, and to enable gaming with  an actor story theory by using the oksimo software tool.

UNIVERSAL TEAM

The classical distinctions between certain types of managers, special experts and the rest of the world is given up here in favor of a stronger generalization: everybody is a potential expert with regard to a future, which nobody knows. This is emphasized by the fact, that everybody can use its usual mother tongue, a normal language, every language. Nothing more is needed.

BASIC MODELS (S, X)

As minimal elements for all possible applications it is assumed here that the experts define at least a given situation (state) [S] and a set of change rules [X].

The given state S is  either (i)  taken as it is or (ii)  as a state which  should be improved. In both cases the initial state S is called the start state [S0].

The change rules X describe possible changes which transform a given state S into a changed successor state S’.

A pair of S and X as (S,X) is called a basic model M(S,X). One can define as many models as one wants.

A DIRECTION BY A VISION V

A vision [V] can describe a possible state SV  in an assumed future. If such a state SV is given, then this state becomes a goal state SGoal In this case  we assume V ≠ 0. If no explicit goal is given, then we assume V = 0.

DEVELOPMENT BY GOALS

If a vision is given (V ≠ 0), then the vision can be used to induce a direction which can/ shall be approached by creating a set X, which enables the generation of a sequence of states with the start state S0 as first state followed by successor state Si until the goal state SGoal has been reached or at least it holds that the goal state is a subset of the reached state: SGoalSn.

It is possible to use many basic models M(S,X) in parallel and for each model Mi one can define a different goal Vi (the typical situation in a pluralistic society).

Thus there can be many basic theories T(M,V) in parallel.

STEADY STATES (V = 0)

If no explicit visions are defined (V = 0) then every direction of change is allowed. A basic steady state theory T(M,V) with V = 0 can   be written as T(M,0). Whether such a case can be of interest is not clear at the moment.

BASIC INTERACTION PATTERNS

The following interaction modes are assumed as typical cases:

  1. N-1: Within an online session an interactive webpage with the oksimo software is active and the whole group can interact with the oksimo software tool.
  2. N-N-1: N-many participants can individually login into the interactive oksimo website and being logged in they can collaborate within the oksimo software with one project.
  3. N-N-N: N-many participants can individually login into the interactive oksimo website and there everybody can run its own process or can collaborate in various ways.

The default case is case (1). The exact dates for the availability of modes (2) – (3) depends from how fast the roadmap can be realized.

BASIC APPLICATIONS
  1. Exploring Simulation-Based Development [ESBD] (V ≠ 0): If the main goal is to find a path from a given state today S (Now) to an envisioned state V in the future then one has  to collect appropriate change rules X to approach the final goal state SGoal better and better. Activating the simulator ∑ during search and construction phase at will can be of great help, especially if the documents (S, X, V) are becoming more and more complex.
  2. Embedded Simulation-Based  Testing [ESBT] (V ≠ 0): If a basic  actor story theory T(M,) is given with a given goal (V ≠ 0) then it is of great help if the simulation is done in interactive mode where the simulator is not applying the change rules by itself but by asking different logged in users which rule they want to apply and how. These tests show not only which kinds of errors will occur but they can also show during n-many repetitions to which degree an user  can learn to behave task-conform. If the tests will not show the expected outcomes then this can point  to possible deficiencies of the software as well to specialties of the user.
  3. Embedded Simulation-Based Gaming [ESBTG] (V ≠ 0):  The case of gaming is partially  different to the case of testing.  Although it is assumed here too that at least one vision (goal) is given, it is additionally assumed that  there exists  a competition between different players or different teams. Different to testing exists in gaming according to the goal(s) the role of a winner: that player/ team which has reached a defined  goal state before the other player/ teams,  has won. As a side-effect of gaming one can also evaluate the playing environment and give some feedback to the developers.
ALGORITHMIC INTELLIGENCE
  1. Case ESBD, T(S,X,V,∑,ε,α): Because a normal simulation with the simulator always does  produce only one path from the start state to the goal state it is desirable to have an algorithm α which would run on demand as many times as wanted and thereby the algorithm α would search for all possible paths and at the same time it would look for those derivations, where the goal state satisfies with  ε certain special requirements. Thus the result from the application of α onto a given model M with the vision V would generate the set SV* of all those final states which satisfy the special requirements.
  2. Case ESBG, T(S,X,V,∑,ε,α):   The case of gaming allows at least three kinds of interesting applications for algorithmic intelligence: (i) Introduce non-biological players with learning capabilities which can act simultaneously with the biological players; (ii) Introduce non-biological players with learning capabilities which have to learn how to support, to assist, to train biological player. This second case addresses the challenging task to develop algorithmic tutors for several kinds of learning tasks. (iii) Another variant of case (ii) is to enable the development of a personal algorithmic assistant who works only with one person on a long-term basis.

The kinds of algorithmic Intelligence in (2)(i)-(iii) are different to the  mentioned algorithmic intelligence α in (1).

TYPES OF ACTORS

As the default standard case of an actor it is assumed that there are biological actors, usually human persons, which will not be analyzed with their inner structure [IS]. While the behavior of every system — and  therefore any biological system too — can be described with a behavior function φ: I x IS —> IS x O (if one has all the necessary knowledge), in the default case of biological systems  no behavior function φ is specified, φ = 0. During interactive simulations biological systems act by themselves.

If non-biological actors are used — e.g. automata with a certain machine program (an algorithm) — then one can use these only if one has a fully specified behavior function φ. From this follows that a  change rule which is associated with a non-biological actor has in its Eplus and in its Eminus part not a concrete expression but a variable, which will be computed during the simulation by the non-biological actor depending from its input and its behavior function φ: φ(input)IS=(Eplus, Eminus)IS.

FINAL COMMENT

Everybody who has read the parts (1) – (4) has now a general knowledge about the motivation to develop the oksimo software tool to support human kind to have a better communication and thinking of possible futures and a first understanding (hopefully :-)) how this tool can work. Reading the UN sustainable development goals [SDGs] [1] you will learn, that the SDG4 (Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all) is fundamental to all other SDGs. The oksimo software tool is one tool to be of help to reach these goals.

REFERENCES

[1] The 2030 Agenda for Sustainable Development, adopted by all United Nations Member States in 2015, provides a shared blueprint for peace and prosperity for people and the planet, now and into the future. At its heart are the 17 Sustainable Development Goals (SDGs), which are an urgent call for action by all countries – developed and developing – in a global partnership. They recognize that ending poverty and other deprivations must go hand-in-hand with strategies that improve health and education, reduce inequality, and spur economic growth – all while tackling climate change and working to preserve our oceans and forests. See PDF: https://sdgs.un.org/sites/default/files/publication/21252030%20Agenda%20for%20Sustainable%20Development%20web.pdf

[2] UN, SDG4, PDF, Argumentation why the SDG4 ist fundamental for all other SDGs: https://sdgs.un.org/sites/default/files/publications/2275sdbeginswitheducation.pdf

 

 

 

 

 

 

 

 

PHILOSOPHY LAB

eJournal: uffmm.org

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

Changes: July 20.2019 (Rewriting the introduction)

CONTEXT

This Philosophy Lab section of the uffmm science blog is the last extension of the uffmm blog, happening July 2019. It has been provoked by the meta reflections about the AAI engineering approach.

SCOPE OF SECTION

This section deals with  the following topics:

  1. How can we talk about science including the scientist (and engineer!) as the main actors? In a certain sense one can say that science is mainly a specific way how to communicate and to verify the communication content. This presupposes that there is something called knowledge located in the heads of the actors.
  2. The presupposed knowledge usually is targeting different scopes encoded in different languages. The language enables or delimits meaning and meaning objects can either enable or  delimit a certain language. As part of the society and as exemplars of the homo sapiens species scientists participate in the main behavior tendencies to assimilate majority behavior and majority meanings. This can reduce the realm of knowledge in many ways. Biological life in general is the opposite to physical entropy by generating auotopoietically during the course of time  more and more complexity. This is due to a built-in creativity and the freedom to select. Thus life is always oscillating between conformity and experiment.
  3. The survival of modern societies depends highly on the ability   to communicate with maximal sharing of experience by exploring fast and extensively possible state spaces with their pros and cons. Knowledge must be round the clock visible to all, computablemodular, constructive, in the format of interactive games with transparent rules. Machines should be re-formatted as primarily helping humans, not otherwise around.
  4. To enable such new open and dynamic knowledge spaces one has to redefine computing machines extending the Turing machine (TM) concept to a  world machine (WM) concept which offers several new services for social groups, whole cities or countries. In the future there is no distinction between man and machine because there is a complete symbiotic unification because  the machines have become an integral part of a personality, the extension of the body in some new way; probably  far beyond the cyborg paradigm.
  5. The basic creativity and freedom of biological life has been further developed in a fundamental all embracing spirituality of life in the universe which is targeting a re-creation of the whole universe by using the universe for the universe.

 

ACI – TWO DIFFERENT READINGS

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

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

HCI – HMI – AAI ==> ACI ?

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

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

HISTORY

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

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

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

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

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

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

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

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

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

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

SOLVING A PROBLEM

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

COGNITION AND AUGMENTED COLLECTIVE INTELLIGENCE

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

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

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

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

ACI ANALYSIS PROCESS

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

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

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

ACTOR STORY (AS)

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

SEVERAL TESTS

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

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

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

COLLECTIVE MEMORY

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

 

 

SIMULATION AND GAMING

eJournal: uffmm.org,
ISSN 2567-6458, 3.May 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

Within the AAI paradigm the following steps will usually be distinguished:

  1. A given problem and a wanted solution.
  2. An assumed context and intended executing and assisting actors.
  3. Assumed non-functional requirements (NFRs).
  4. An actor story (AS) describing at least one task including all the functional requirements.
  5. An usability test, often enhanced with passive or interactive simulations.
  6. An evaluation of the test.
  7. Some repeated improvements.

With these elements one can analyze and design the behavior surface of an  assistive actor which can approach the requirements of the stakeholder.

SIMULATION AND GAMING

Comparing these elements with a (computer) game then one can detect immediately that  a game characteristically allows to win or to lose. The possible win-states or lose-states stop a game. Often the winning state includes additionally  some measures how ‘strong’ or how ‘big’ someone has won or lost a game.

Thus in a game one has besides the rules of the game R which determine what is allowed to do in a game some set of value lables V which indicate some property, some object, some state as associated with some value v,  optionally associated further with some numbers to quantify the value between a maximum and a minimum.

In most board games you will reach an end state where you are the winner or the loser independent of some value. In other games one plays as often as necessary to reach some accumulated value which gives a measure who is better than someone else.

Doing AAI analysis as part of engineering it is usually sufficient to develop an assistive actor with a behavior surface  which satisfies all requirements and some basic needs of the executive actors (the classical users).

But this newly developed product (the assistive actor for certain tasks) will be part of a social situation with different human actors. In these social situations there are often more requirements, demands, emotions around than only the original  design criteria for the technical product.

For some people the aesthetic properties of a technical product can be important or some other cultural code which associates the technical product with these cultural codes making it precious or not.

Furthermore there can be whole processes within which a product can be used or not, making it precious or not.

COLLECTIVE INTELLIGENCE AND AUTOPOIETIC GAMES

In the case of simulations one has already from the beginning a special context given by the experience and the knowledge of the executive actors.  In some cases this knowledge is assumed to be stable or closed. Therefore there is no need to think of the assistive actor as a tool which has not only to support the fulfilling of a defined task but additionally to support the development of the knowledge and experience of the executive actor further. But there are social situations in a city, in an institution, in learning in general where the assumed knowledge and experience is neither complete nor stable. On the contrary in these situations there is a strong need to develop the assumed knowledge further and do this as a joined effort to improve the collective intelligence collectively.

If one sees the models and rules underlying a simulation as a kind of a representation of the assumed collective knowledge then  a simulation can help to visualize this knowledge, make it an experience, explore its different consequences.  And as far as the executive actors are writing the rules of change by themselves, they understand the simulation and they can change the rules to understand better, what can improve the process and possible goal states. This kind of collective development of models and rules as well as testing can be called autopoietic because the executing actors have two roles:(i)  following some rules (which they have defined by themselves) they explore what will happen, when one adheres to these rules; (ii) changing the rules to change the possible outcomes.

This procedure establishes some kind of collective learning within an autopoietic process.

If one enriches this setting with explicit goal states, states of assumed advantages, then one can look at this collective learning as a serious pattern of collective learning by autopoietic games.

For many context like cities, educational institutions, and even companies  this kind of collective learning by autopoietic games can be a very productive way to develop the collective intelligence of many people at the same time gaining knowledge by having some exciting fun.

Autopoietic gaming as support for collective learning processes
Autopoietic gaming as support for collective learning processes