INTELLIGENT DECISION SUPPORT

SYSTEM:

TOGA COGNITIVE AGENT

INTELLIGENT DECISION SUPPORT INTELLIGENT DECISION SUPPORT

SYSTEM:SYSTEM:

TOGA COGNITIVE AGENTTOGA COGNITIVE AGENT

Adam M.Gadomski Franco Pestilli

The ECONA’s Meeting on “ Research Activities on Cognitive Modeling”

13th May 1999, Rome, The University of Rome “La Sapienza”

ECONA - Centro Interuniversitario di Ricerca sull'Elaborazione Cognitiva in Sistemi Naturali e Artificiali

The activity is realized in frame of the R&D cooperation of ECONA and ENEA:

M.Olivetti-Belardinelli (ECONA),

A.M.Gadomski (ENEA), F.Pestilli(Psych.Dep. Univ. La Sapienza, ENEA scholarship)

Step 1: Introduction to Intelligent Decision Support SystemsStep 2: Intelligent Agent Approach to IDSS

- - Intelligent Agent definition

- - Intelligent Agent Approach

Step 3: IDSS Definition - TOGA (Top-down Object-based Goal-

oriented Approach) HypothesisStep 4: TOGA BasesStep 5: TOGA Cognitive Agent: IPK Architecture

- - IPK concepts

- - IPK systems

Step 6: Planned Work - Experimental identification of IPK-IA

reasoning- - Work plan

- - Experimental data

- - TOGA Conceptualization

References

PRESENTATION SUMMARYPRESENTATION SUMMARY

IDSS

“Software program that integrates human intellectual capacities and computer capacities to improve decision making quality, in semi-structured problems situations”[Keen, Scott-Morton, 1996]

IDSS

“Software program that integrates human intellectual capacities and computer capacities to improve decision making quality, in semi-structured problems situations”[Keen, Scott-Morton, 1996]

- Active Advisor

- Cognitive InterfaceProvide Decisional Aid

To Human Operators/ Managers

TO

Vs

DSSDSS - Passive Advisor

- Information Provider

Provide Informational Aid

To Human Operators/ ManagersTO

INTRODUCTION TO IDSSINTRODUCTION TO IDSS

IDSSIDSS

-The amount of information necessary for the management is so large, or its time density is so high, that the probability of human errors during emergency is not negligible.

-The coping with unexpected by managers (and DSS designer) situation requires from them the remembering, mental elaboration and immediate application of complex professional knowledge, which if not properly used, causes fault decisions.

-The amount of information necessary for the management is so large, or its time density is so high, that the probability of human errors during emergency is not negligible.

-The coping with unexpected by managers (and DSS designer) situation requires from them the remembering, mental elaboration and immediate application of complex professional knowledge, which if not properly used, causes fault decisions.

IDSS are especially important when:IDSS are especially important when:

IDSSIDSS

ROUTINEROUTINE

EMERGENCYEMERGENCYBUSINESSBUSINESS

INDUSTRYINDUSTRY

HEALTHHEALTH

INFORMATION ACQUISITION

INFORMATION ACQUISITION

SERVICES &ADMINISTRATION

SERVICES &ADMINISTRATION

RESEARCH

APPLICATION

CONTEXTNOWNOW

3

INTELLIGENT AGENT APPROACH TO IDSS CONSTRUCTION

IINTELLIGENT NTELLIGENT AAGENT GENT AAPPROACH TO PPROACH TO IDSSIDSS CONSTRUCTIONCONSTRUCTION

An AgentAgent is an entity, an object, which is able to execute a class of symbolic tasks, that autonomously react to some changes of its environment.

An Agent could be considered an element of:

Software World: Where the Agent is a “soft-agent” interacting only with software entities in a computer software world. This world can be distributed among different types of computers. The software agents execute in more or less autonomous, or in more or less intelligent manner. In this way the soft-agent “lives” in abstract symbolic worlds composed by files, directories, servers, all being carried by computer.

Physical World: In this the agent is a cognitive and engineering attempt at the explanation, modeling and simulation of human mental functions. The agent or intelligent agent is analyzed as some abstraction from human person to the specification of various: professional, social and psychological roles; these agents’ environment is a vision composed with preselected aspects of the real world. They need to act autonomously or to support human interventions. They “live” in various simulations of the world or acts directly in the physical never completed description of a domain.

Intelligent agent is an agent with the capability of learning, changing its own goals and reasoning on different abstraction levels.

INTELLIGENT AGENTINTELLIGENT AGENTINTELLIGENT AGENT

Intelligent Agent Approach

Intelligent Agent flexibility, modularity and reusing depend

strongly on the type of architecture accepted.

Intelligent Agent flexibility, modularity and reusing depend

strongly on the type of architecture accepted.

- Multi-agent architecture is based on a modular, repetitive structure, with the possibility of (user friendly) modifications of the specific emergency domain, and

- User roles modelling are considered as key research fields in the IDSS development.

-- Applied to the Decision Support System theory, Applied to the Decision Support System theory, IAAIAA offers an integrated offers an integrated conceptualisation of various communication and reasoning tools conceptualisation of various communication and reasoning tools in the form of in the form of autonomous executors of mental tasks which support humans’ deautonomous executors of mental tasks which support humans’ decisioncision--making making processes.processes.

-- Intelligent agentIntelligent agent has capabilities of: information has capabilities of: information filteringfiltering and and interpretationinterpretationaccording to the human role and situation model. It may suggest according to the human role and situation model. It may suggest new goals, new goals, alternative decisions or elaborates plans of intervention.alternative decisions or elaborates plans of intervention.

-- Intelligent agentIntelligent agent can use various Artificial Intelligent methods which enable to can use various Artificial Intelligent methods which enable to copy with uncertain and incomplete data, such as Case Based Reascopy with uncertain and incomplete data, such as Case Based Reasoning, Rules oning, Rules Based Learning and so on.Based Learning and so on.

Intelligent Agent Approach (IAA):

AIA MODEL

AIA AIA MODELMODEL

SYSTEM

ARCHITECTURE

IDSSIDSSIDSS

USER

REPRESENTATION

From the above point of view, IDSS requires:

- An intelligent system architecture (it can be done by the specification of an intelligent modular kernel-architecture, IPK architecture)

- An user (role) model (it can be done by the extraction from the human cognitive descriptions that represents: human goal oriented behaviour, Cognitive experiment). Roughly speaking the user model should include: a reasoning “motor”, role representation sub-system and a psychological profile of the user for the modification of the rational reasoning. (N.B. this last element, psychological profile, is not yet represented in TOGA Intelligent Agent).

From the above point of view, IDSSIDSS requires:

- An intelligent system architecture (it can be done by the specification of an intelligent modular kernel-architecture, IPK architecture)

- An user (role) model (it can be done by the extraction from the human cognitive descriptions that represents: human goal oriented behaviour, Cognitive experiment). Roughly speaking the user model should include: a reasoning “motor”, role representation sub-system and a psychological profile of the user for the modification of the rational reasoning. (N.B. this last element, psychological profile, is not yet represented in TOGA Intelligent Agent).

The TOGA Hypothesis of the IDSS definition

In this context, TOGA intents to have utility consensus, as a consequence, it should also has explicit and natural consensus in selected application domains.

In modelling, personal experience and mental historical records are considered an acceptable source of information to the definition of the necessary characteristics of the model [G.Polya, 1957; A.Newell, 1972]. This is a especially valid for cognitivisticconstructions.Main problems referred to this task is the lack of explicit consensus on such products, and natural consensus on their models.

For example, according to [ Gadomski 1990], verification of a model/theory is based on:

-- IMPLICIT CONSENSUS, in a predefined human community, when the individual motivations of one member are not known by the others;-- UTILITY CONSENSUS, when successful application of the model/theory to the solution of selected practical problems has been performed (engineering paradigm);-- EXPLICIT CONSENSUS, when an accepted theory or another conceptualisation system has been established and a proof can be/is given inside it;-- NATURAL CONSENSUS, when experimental verification of the model/theory is performed (cognitive paradigm).

In modelling, personal experience and mental historical records are considered an acceptable source of information to the definition of the necessary characteristics of the model [G.Polya, 1957; A.Newell, 1972]. This is a especially valid for cognitivisticconstructions.Main problems referred to this task is the lack of explicit consensus on such products, and natural consensus on their models.

For example, according to [ Gadomski 1990], verification of a model/theory is based on:

-- IMPLICIT CONSENSUS, in a predefined human community, when the individual motivations of one member are not known by the others;-- UTILITY CONSENSUS, when successful application of the model/theory to the solution of selected practical problems has been performed (engineering paradigm);-- EXPLICIT CONSENSUS, when an accepted theory or another conceptualisation system has been established and a proof can be/is given inside it;-- NATURAL CONSENSUS, when experimental verification of the model/theory is performed (cognitive paradigm).

Initial Consensus

TOGA(TOP-DOWN OBJECT-BASED GOAL-ORIENTED APPROACH)

TOGATOGA(TOP-DOWN OBJECT-BASED GOAL-ORIENTED APPROACH)

This conceptual frame has been proposed and developed by Gadomski [Gadomski, 89,93]:

- an approach to complex problem specification

- an approach to decision-making modelling

Enables representation of “intelligent” activities of artificial dynamic systems

TOGA (meta-theory) IS COMPOSED OF 3 BASIC ELEMENTS:

- Theory of Abstract Objects (TAO)- Knowledge Conceptualisation System (KNOCS) - Methodological Rules System (MRUS)

TAO is a mathematical construction and domain independent conceptualization system for symbolic human goal-oriented activity, it is based on four important concepts:

-- Object is any abstract entity specified by its, name, attributes,

values, values-domain;

-- Relation is any kind of “link” between two objects, specified by its

name and object attributes;

-- Change(s) is any operation that produce new state of the world of object

-- World of object (w-o-o) is any isolated network of objects and relations;

-- Universe(s) is any set of ws-o-o linked by its element names, where the attribute name from the precedent w-o-o is an object name in the other one;

KNOCSTOGA starts from basic assumption of the existence of an abstract intelligent agent and its domain of activityTherefore, KNOCS is a system of axioms and definitions for the description of interactions between the Abstract Intelligent Agent (AIA) and its environment; it enables the conceptualization of different physical system such as industrial plants, robots, human operators or organization.

ll Domain of Activity; ll AIA Model; ll Goal-oriented Activity;

MRUS is a methodological approach to knowledge ordering for the specification of complex problems (Top-down Knowledge Ordering); the fundamental MRUS strategies refers to problem identification, specification and methodology of problem solvingin the context of TAO and KNOCS:

-- Top-down mechanism: it works from very abstract level to details of the problem;

-- Goal-driven mechanism: it controls links between the particular object and the goal (using the Point of View concept).

TOGA COGNITIVE AGENTIPK (INFORMATION, PREFERENCES, KNOWLEDGE) ARCHITECTURE

TOGA COGNITIVE AGENTTOGA COGNITIVE AGENTIPKIPK (INFORMATION, PREFERENCES, KNOWLEDGE) ARCHITECTUREARCHITECTURE

IPK is:- A TOGA approach to generic goal-oriented reasoning- An abstraction of useful characteristics to AIA identification

IPK is:- A TOGA approach to generic goal-oriented reasoning- An abstraction of useful characteristics to AIA identification

THEORICALTHEORICAL

IPK is:- a carrier of goal oriented reasoning processes- an abstract architecture to AIA identification

IPK is:- a carrier of goal oriented reasoning processes- an abstract architecture to AIA identification

PRACTICALPRACTICAL

IPKIPK architecture satisfies the following three basic assumptions for a well structured cognitive model:

-- Recursivity-- Repetitivity-- Modularity

(these assumptions minimize global information)

I, P, K concepts consist of a triple definition. It means they have to be only defined together. These concepts don’t exists in separate manner, but they are independent.

IPK is based on these basic concepts:- Information-

-how situation looks like-presents states of D-O-A

- Preferences --an order of hypotetic states of D-O-A -what is more important

- Knowledge --how situation can be modeled

(descriptive) -what is possible to do (operational)

- goal - -what state should be achieved

KNOWLEDGE

PRFERENCE

Info. 1 Info. 2

Goal 1

IPK CONCEPTSIPK CONCEPTS

In IPK ARCHITECTURE an Abstract simple agentsimple agent is represented by three systems:

Domain representation System, Domain representation System, DSDSStructure:

_Representation of d-o-a;_ conceptualization mechanism.

Function:_Transforms signals from d-o-a in information;_Information is memorized and is sent to the preference system.

Preference System, Preference System, PSPSStructure:

_Preferences rules bases, PRB;_Intervention-goal generating mechanism.

Function:_It is activated by information coming from the DS;_Determinates which state is more important (decides goal).

Knowledge System, Knowledge System, KSKSStructure:

_Knowledge rules bases, KRB;_Intervention-procedure generating mechanism (descriptive, operational)

Function:_It is activated by goal coming coming from the PS;_Determinates what must/should be done.

IPK SYSTEMSIPK SYSTEMS

MonadMonad is represented graphically as a triangle System composed of three modules: DS. PS, KSDS. PS, KS.

- A Monad is not an intelligent agent- Monads may be hierarchically organized- A Monads’ hierarchy may be seen as an abstract intelligent agent

An abstract simple agentsimple agent is called MonadMonad.

MonadMonad

KSKSKS

Inf.

goal

Inf.

Inf.

PSPSPS

DSDSDS

AIA REPRESENTED AS A MONADS’ HIERARCHICAL ORGANIZATIONAIA REPRESENTED AS A MONADS’ HIERARCHICAL ORGANIZATION

- A Monad is a IPK module

- Monads’ hierarchy has a recursive and repetitivestructure

DSDS

KSKSPSPS

I LEVEL

I META-LEVEL

pKSpKSpPSpPS

pDSpDS

kPSkPSkPSkPS

kDSkDS

This type of the AIA is called PERSONOID

(We can imagine a fractal evolution of such agent…[Gad. Web])

This type of the AIA is called PERSONOID

(We can imagine a fractal evolution of such agent…[Gad. Web])

PRESENTATION'S STRUCTUREPRESENTATION'S STRUCTURE

Step 1: Introduction to Intelligent Decision Support SystemsStep 2: Intelligent Agent Approach to IDSS

- - Intelligent Agent definition

- - Intelligent Agent Approach

Step 3: IDSS Definition - TOGA (Top-down Object-based Goal-oriented Approach) Hypothesis

Step 4: TOGA BasesStep 5: TOGA Cognitive Agent: IPK Architecture

- - IPK concepts

- - IPK systems

Step 6: Planned Work - Experimental identification of IPK-IA reasoning- - Work plan

- - Experimental data

- - TOGA Conceptualization

References

- In order to realize Functions (goal) of Intelligent Agent

- Having a Structure => IPK

- An identification of the Processes is necessary

PLANNED WORK - EXPERIMENTAL IDENTIFICATION OF IPK-IA REASONING

PLANNED WORK - EXPERIMENTAL IDENTIFICATION OF IPK-IA REASONING

Hypothesis: we intend to use human cognitive processes, obtained

from experiment, in order to identify the needed processes to be inserted

in an IPK architecture (This possibility results from the previous cognitive

research of Olivetti-Belardinelli).

In this way, we are going to:

- compare the TOGA model response and the human cognitive behavior;

- validate the TOGA model utility for IDSS design.

Hypothesis: we intend to use human cognitive processes, obtained

from experiment, in order to identify the needed processes to be inserted

in an IPK architecture (This possibility results from the previous cognitive

research of Olivetti-Belardinelli).

In this way, we are going to:

- compare the TOGA model response and the human cognitive behavior;

- validate the TOGA model utility for IDSS design.

We plan to use humans problem solving tests to identify a personoids’“ideal” reasoning

We plan to use humans problem solving tests to identify a personoids’“ideal” reasoning

WORK PLANWORK PLAN

Empirical Data

Reasoning Processesextraction

IPKconceptualisation

IPKcomputer

implementation

Simulation frame

Test Case ResultsQualitative confrontation

It is possible to divide the above described hypothesis in the sequent way:It is possible to divide the above described hypothesis in the sequent way:

Manager’s ProblemStudent’s Problem

EXPERIMENTAL DATAEXPERIMENTAL DATA

We intend to use data from the experiment of M. Olivetti Belardinelli & E. Pessa : “Strategies for solving physics problem in naive subjects”; in which the authors use a physics problem as an example of the general problem solving situation.They present 20 simple electric circuits exercises to 120 not-expert subjects (students of Psychology) to be resolved without ending time, but describing steps of the processes they use to solve.

We intend to use data from the experiment of M. Olivetti Belardinelli & E. Pessa : “Strategies for solving physics problem in naive subjects”; in which the authors use a physics problem as an example of the general problem solving situation.They present 20 simple electric circuits exercises to 120 not-expert subjects (students of Psychology) to be resolved without ending time, but describing steps of the processes they use to solve.

This picture represent the electric circuit used to construct the 20 problems presented to each subject.

This picture represent the electric circuit used to construct the 20 problems presented to each subject.

At the beginning of the experiment, short indications aboutthe picture was given.

Then a set of relations between the symbols of the electric circuit’s objects and variables are presented.

At the beginning of the experiment, short indications aboutthe picture was given.

Then a set of relations between the symbols of the electric circuit’s objects and variables are presented.

The picture above represent an electric circuit that is composed of different elements (resistances, whom values are indicated by r,t,s symbols; a generator characterized by a potential, symbolized by V ). It is not important if you know nothing about electric circuits theory, you can try to solve problems dealing with it, you should act to find variables value starting from knowledge about rules that regulates the circuit.

We present a set of this rules:

1) R = r + [ s t / ( s + t ) ]2) V = u + v3) V = R I4) U = r I5) V = s i6) V = t j7) I = i + j8) P = V I

EXPERIMENTEXPERIMENT

Data are organized in protocols, every subject should describe all steps his/her thinks are needed to achieve the goal (which could also be an intermediate goal towards the solution).

These protocols are similar to the sequent:

Data are organized in protocols, every subject should describe all steps his/her thinks are needed to achieve the goal (which could also be an intermediate goal towards the solution).

These protocols are similar to the sequent:

STEP n

A) KNOWN VALUES

r s t R u v V I j I P

B) VALUES TO ACHIEVE

r s t R u v V I j I P

C) USED RULE

1 2 3 4 5 6 7 8

STEP n1

A) . . . . .

B) . . . . .

C) . . . . .

The first point we work on is the insertion of the experimental domain of activity (the electric circuit) into the TOGA framework.

The TOGA approach is a meta-systemic approach in which the distinction between subject and object (agent and environment) is crucial, its description depends on the level of abstraction and our goals accepted.From this point of view the first important thing to do in definingan IA is the insertion (transformation/translation) of its environment into the TOGA’s world of object.

TOGA fundamental assumption on it is that we may represent with TOGA’s methodology every intelligent agent’s domain of activity.

TOGA CONCEPTUALIZATIONTOGA CONCEPTUALIZATION

The experimental data have been obtained in subsequent steps of thinking processes.In order to use them to construct the agent we must identify a relation between human data and the IPK architecture; Especially, it refers to the mechanisms of preferences and knowledge choices.

The idea is to use the given relations (physical rules) as a knowledge of the Knowledge System. In this way we should recognize which physical rules are used on every problem solving step.

In the experiment, we can describe the reasoning process of human agents using the following re-conceptualization:

“Known values” (A) are information (it dependents on the domain state) in the TOGA terminology.“Values to achieve ” (B) are attributes of agent goals specification in the TOGA terminology - the sequence of the temporal goals are ordered according to individual preferences.“Used rules” (C) are the KS knowledge involved in the process at every step.

- From the experimental results we expect to obtain the accepted preferences rules.

TOGA CONCEPTUALIZATION, 2TOGA CONCEPTUALIZATION, 2

ELECTRIC CIRCUIT REPRESENTATION USING TOGA METHODOLOGY,1

Initial “classical” representation of Information

LEV 1

LEV 2

CELL

R 1

R 2.1

R 2.2

CELL

R 1

R 2

CIRCUIT

Rt

AGENT

LEV 4

LEV 3

CELL

D-O-A:CIRCUIT

The electric circuit representation using the TAO theoryThe electric circuit representation using the TAO theory

ELECTRIC CIRCUIT REPRESENTATION USING TOGA METHODOLOGY, 2

• The TOGA model does not refer to any “absolute truth”.

• In this sense TOGA does not refer to an “Absolute Human Mind” model, but it should be useful to describe Human Mind activities from different specific points of view.

• The goal is to be useful in the engineering constructions of mental processes during goal-oriented activities.

• We are not interested in how the “really” environment is organized, but how this organization could be represented for our goals, and for the goal of AIA.

• The TOGA model does not refer to any “absolute truth”.

• In this sense TOGA does not refer to an “Absolute Human Mind” model, but it should be useful to describe Human Mind activities from different specific points of view.

• The goal is to be useful in the engineering constructions of mental processes during goal-oriented activities.

• We are not interested in how the “really” environment is organized, but how this organization could be represented for our goals, and for the goal of AIA.

WARNINGSWARNINGS

REFERENCES, 1REFERENCES, 1

- M. Olivetti-Belardinelli, E. Pessa “Strategies for solving Physics problem in naive subjects”, ESCOP Conference, 1993.

- A. M. Gadomski “Intelligent-Agents’ Worlds in the TOGA Conceptualization”;- Ed. Swanstrom “KMC - Glossary of Terms for rewiew”,1999.- A. M. Gadomski “An application of System-Process-Goal Approach /SPG/ to the

TRIGA RC-1 Reactor System description”;1988.- C. Balducelli, S. Bologna, G. Di Costanzo, A. M. Gadomski, G. Vicoli “Computer

aided training for cooperating emergency managers: some results of MUSTER project”, TIEMC Conf. 1996.

- A. M. Gadomski, C. Balducelli, S. Bologna, G. Di Costanzo “Integrated ParallelBottom-up and Top-down Approach to the Development of Agent-based Intelligent DSSs for Emergency Management”;1998.

- A. M. Gadomski “TOGA - A Methodological and Conceptual Pattern for Modelling Abstract Intelligent Agent”, Proceedings of AIA93, 1994;

- A. M. Gadomski, V. Nanni, S. Taglienti “Some Theoretical and PracticalAspects of Modelling Abstract Intelligent Agent: ENEA’sExperiences”;1994.

- M. Olivetti-Belardinelli, E. Pessa “Strategies for solving Physics problem in naive subjects”, ESCOP Conference, 1993.

- A. M. Gadomski “Intelligent-Agents’ Worlds in the TOGA Conceptualization”;- Ed. Swanstrom “KMC - Glossary of Terms for rewiew”,1999.- A. M. Gadomski “An application of System-Process-Goal Approach /SPG/ to the

TRIGA RC-1 Reactor System description”;1988.- C. Balducelli, S. Bologna, G. Di Costanzo, A. M. Gadomski, G. Vicoli “Computer

aided training for cooperating emergency managers: some results of MUSTER project”, TIEMC Conf. 1996.

- A. M. Gadomski, C. Balducelli, S. Bologna, G. Di Costanzo “Integrated ParallelBottom-up and Top-down Approach to the Development of Agent-based Intelligent DSSs for Emergency Management”;1998.

- A. M. Gadomski “TOGA - A Methodological and Conceptual Pattern for Modelling Abstract Intelligent Agent”, Proceedings of AIA93, 1994;

- A. M. Gadomski, V. Nanni, S. Taglienti “Some Theoretical and PracticalAspects of Modelling Abstract Intelligent Agent: ENEA’sExperiences”;1994.

- A. M. Gadomski “IPK: Information, Preferences, Knowledge” (Web), 1997;- A. M. Gadomski “Intelligent Agents’ Worlds in the TOGA Conceptualization”;- A. M. Gadomski “Paradigms of Personoids,;(Web);- A. M. Gadomski “Agents and Intelligence”,(Web);- A. M. Gadomski, G. Di Costanzo “Intelligent Decision Support System for

Industrial Accident Management: Abstract Intelligent AgentBased Modelling”, 1996;

- A. M. Gadomski “Action-Oriented Decision-Making: TOGA MethodologicalApproach”, Draft version.1992;(Web);

- A. M. Gadomski “TOGA Methodology: Top-down, Object-based, Goal-orientedApproach to Knowledge Ordering”, JRC Ispra Seminar, 1989;

- A. M. Gadomski, V. Nanni “Intelligent Computer Aid For Operators: TOGA Based Conceptual Framework”;Singapore,1993;

- A. M. Gadomski, S. Bologna, G. Di Costanzo “Intelligent Decision Support for Cooperating Emergency Managers: The TOGA BasedConceotuallization Framework”, TIEMEC95, 1995;

- A.M.Gadomski, S. Bologna, G. Di Costanzo, A. Perini, M. Schaerf “An Approach to the Intelligent Decision Advisor (IDA) for Emergency Managers”,Process. of TIEMS’99 Conference,1999, (Web);

- A. M. Gadomski “IPK: Information, Preferences, Knowledge” (Web), 1997;- A. M. Gadomski “Intelligent Agents’ Worlds in the TOGA Conceptualization”;- A. M. Gadomski “Paradigms of Personoids,;(Web);- A. M. Gadomski “Agents and Intelligence”,(Web);- A. M. Gadomski, G. Di Costanzo “Intelligent Decision Support System for

Industrial Accident Management: Abstract Intelligent AgentBased Modelling”, 1996;

- A. M. Gadomski “Action-Oriented Decision-Making: TOGA MethodologicalApproach”, Draft version.1992;(Web);

- A. M. Gadomski “TOGA Methodology: Top-down, Object-based, Goal-orientedApproach to Knowledge Ordering”, JRC Ispra Seminar, 1989;

- A. M. Gadomski, V. Nanni “Intelligent Computer Aid For Operators: TOGA Based Conceptual Framework”;Singapore,1993;

- A. M. Gadomski, S. Bologna, G. Di Costanzo “Intelligent Decision Support for Cooperating Emergency Managers: The TOGA BasedConceotuallization Framework”, TIEMEC95, 1995;

- A.M.Gadomski, S. Bologna, G. Di Costanzo, A. Perini, M. Schaerf “An Approach to the Intelligent Decision Advisor (IDA) for Emergency Managers”,Process. of TIEMS’99 Conference,1999, (Web);

REFERENCES, 2REFERENCES, 2

- 0. There exist only an intelligent agent and its environment, both of them are decomposable on agents and environments.

- 1. Information, Preferences and Knowledge (IPK) are only and always internal (individual/collective) properties of agents.

- 2. IPK are application-context dependent, they are relative concepts.

- It means that the same expression/sentence/predicate can be seen as information and as knowledge from two different perspectives.

- Any knowledge can be considered (re-conceptualized) as information but not every information can be seen as knowledge.

- 3. Any IPK component is always related to a pre-selected domain of activity (real or mental), D.

- An abstract agent can distinguish various sub-activity domains in its own environment (real and mental).

Some Basic Meta-ontological assumptions of TOGASome Basic Meta-ontological assumptions of TOGA

ANNEX