Top-Down Incremental Development Top-Down Incremental Development of of Agents' Architecture for Agents' Architecture for

Emergency Emergency Management Systems:Management Systems:

TOGA methodologyTOGA methodology

HID, CAMO Seminars Series

This activity is realized in cooperation between La Sapienza University and ENEA: F.Delli Priscoli This activity is realized in cooperation between La Sapienza University and ENEA: F.Delli Priscoli (Univ. La Sapienza, Rome), A.M.Gadomski ((Univ. La Sapienza, Rome), A.M.Gadomski (CAMO, CAMO, ENEA), A.Caputo -ENEA), A.Caputo - thesis thesis (Univ. La Sapienza - (Univ. La Sapienza - Engineering Dep., ENEA scholarship 2002/0362)Engineering Dep., ENEA scholarship 2002/0362)

Andrea Caputo, Adam Maria Gadomski, Franco Delli Priscoli

May 2005

University of Rome “La Sapienza” Italian National Research Agency ENEA

Top-Down Incremental Development of Intelligent Top-Down Incremental Development of Intelligent Agents' ArchitectureAgents' Architecture

• Intelligent Intelligent Agents' Architecture: Agents' Architecture: Problem Specification Problem Specification

• Existing Design & Programming styles (short soa)Existing Design & Programming styles (short soa)

• TOGA Theoretical ToolTOGA Theoretical Tool

• Method: Top-Down incremental developmentMethod: Top-Down incremental development

• Emergency Management Test-CaseEmergency Management Test-Case

• ConclusionsConclusions

• Prototype demonstrationPrototype demonstration

Presentation outlinePresentation outline

Contents of the Caputo’s ThesisContents of the Caputo’s Thesis

• General request overiview General request overiview

• Contest of the simulation: Socio-Cognitive EngineeringContest of the simulation: Socio-Cognitive Engineering

• A TOGA proposalA TOGA proposal

• IPK monadIPK monad

• Universal Management ParadigmsUniversal Management Paradigms

• Example showed at SCEF 2003Example showed at SCEF 2003

• Intelligent Decision Support SystemIntelligent Decision Support System

• Modelling Disaster DomainModelling Disaster Domain

• Disaster PropagationDisaster Propagation

• GEAGEA

Natural Natural SciencesSciences

SoftwareSoftwareTechnologyTechnology

ArtificialArtificialIntelligenceIntelligence

Socio-CognitiveSocio-CognitiveEngineeringEngineering

Contest of the SimulationContest of the Simulation

From the Socio-cognitive contest we will arrive at a From the Socio-cognitive contest we will arrive at a ripetitive, incremental, ricorsive, distribuiteripetitive, incremental, ricorsive, distribuite

INTELLIGENT ENTITY [ 1 ] INTELLIGENT ENTITY [ 1 ]

IPKIPK InformationsInformations ( I )( I ) PreferencesPreferences ( P )( P ) KnowledgesKnowledges ( K )( K )

I’ = KI’ = Kx x II I, I’ I, I’ D DDD

KKxx K K

KKxx = = PPss (K, I) (K, I)

UMPUMPUniversal Management ParadigmUniversal Management Paradigm

(UMP) is a (UMP) is a functional architecturefunctional architecture

of organizational High-Intelligenceof organizational High-Intelligence

for every natural and artificial High-for every natural and artificial High-

Intelligent agents’ organization.Intelligent agents’ organization.

It is characterized by:It is characterized by: CompleteComplete

RelativeRelative

RecursiveRecursive

IncrementalIncremental

IPKIPK paradigmparadigm and and UMPUMP describe essential functional properties of describe essential functional properties of abstract highly intelligent entities, natural and artificialabstract highly intelligent entities, natural and artificial.

A TOGA PROPOSAL A TOGA PROPOSAL [ 2 ][ 2 ]

II

KKPP

SOCIO-COGNITIVE ENGINEERING PARADIGMSSOCIO-COGNITIVE ENGINEERING PARADIGMS

structural assumptions:structural assumptions:

-- -- RecursivityRecursivity

-- Iterativness-- Iterativness

-- Repetitivity-- Repetitivity

-- Modularit-- Modularityy

They They intend to intend to minimize total axiomatic information employedminimize total axiomatic information employed by the theory. by the theory.

  

methodological assumptions, which require completeness and congruence methodological assumptions, which require completeness and congruence

of of the problem conceptualization on every abstraction level. the problem conceptualization on every abstraction level.

       terminological assumption, to reduce the number of terms as is possibleterminological assumption, to reduce the number of terms as is possible..

The key The key TOGATOGA paradigms paradigms (top (top assumptions/axiomsassumptions/axioms)) are divided on are divided on [ 3 ][ 3 ] : :

ConceptualizationConceptualization, , OntologicalOntological, and, and Methodological Methodological

TOGA TOGA NNormative ormative MMeta-eta-AAssumptionsssumptions

Three components:Three components:

TAO :TAO : Basic conceptualization frame Basic conceptualization frame

independent on represented domain ofindependent on represented domain of interest.interest.

KNOCS :KNOCS : Axioms system for the real-world problem representationAxioms system for the real-world problem representation

MRUS :MRUS : Methodological RUles SystemsMethodological RUles Systems

Non ordered Non ordered observations, observations, knowledge, knowledge, values values

TAOTAO ConceptualizationsConceptualizations

KNOCS KNOCS ConceptualizationConceptualization

Goal-oriented Goal-oriented Problem Problem ModelModel

MRUS: Methodological Rules SystemMRUS: Methodological Rules System

They refers to an Abstract Intelligent Agent (AIA), his/her/its Domain-of-Activity They refers to an Abstract Intelligent Agent (AIA), his/her/its Domain-of-Activity and to the relations between them.and to the relations between them.

Summarizing, what is itSummarizing, what is it ?? • • Complex-Knowledge Ordering Methodology (Meta-theory)Complex-Knowledge Ordering Methodology (Meta-theory) • • Problem Specification & Decision-Making Modelling Approach. (It has algebra property)Problem Specification & Decision-Making Modelling Approach. (It has algebra property)

TOGA Meta-Modeling FrameworkTOGA Meta-Modeling Framework

Personois: IPK Abstract AgentPersonois: IPK Abstract Agent

• Model AxiomsModel Axioms

RepetivetyRepetivety

ModularityModularity

RecursivityRecursivity

……

II

PP KK

PP KK PP KK

I LEVELI LEVEL

II II

II META-LEVELII META-LEVEL

Universal Management ParadigmUniversal Management Paradigm

COOPERATINGCOOPERATINGMANAGERMANAGERMANAGERMANAGERADVISORADVISOR

SUPERVISORSUPERVISOR

EXECUTOREXECUTORINFORMERINFORMER

INFORMATIONINFORMATION TASKSTASKS

EXPERTISESEXPERTISES COOPERATIONCOOPERATION

TASKSTASKS INFORMATIONINFORMATION

DISASTER DOMAINDISASTER DOMAINDISASTER DOMAINDISASTER DOMAIN

Based Structure: Based Structure: Subjective, Subjective, Incremental, Incremental, Recursive Recursive

Ref. [ 4 ] Ref. [ 4 ]

Disaster Manager: simple model exampleDisaster Manager: simple model example

IInn

KKPP

II11

KKPP

II22

KKPP

II33

KKPP

Infrastructure NetworkInfrastructure NetworkReal Emergency Domain

- - -- - -

II

KKPPAgent ManagerAgent Manager

Agent 1Agent 1 Agent 2Agent 2 Agent 3Agent 3 Agent nAgent n

I : InformationI : Information

P : PreferencesP : Preferences

K : KnowledgeK : Knowledge

Objectives of experiment: why?Objectives of experiment: why?

Practical vefification of the methodology by the Practical vefification of the methodology by the designing a series of agents with incremental designing a series of agents with incremental

complexity and functionality.complexity and functionality.

The prototypes have been developed in Object oriented C++ language.

As a test case, we assumed an emergency situation caused by

An explosion in a chemical plant where its consequences cause

An intoxication of the water in a neighboring city.

On the base of the TOGA paradigms, we built an On the base of the TOGA paradigms, we built an evolution lineevolution line of the of the incremental design of Intelligent Agents aimed at the development of the model incremental design of Intelligent Agents aimed at the development of the model

of an of an Intelligent EntityIntelligent Entity

The representation of the abstract world of the Agent isThe representation of the abstract world of the Agent is::

WORLD WORLD ANIMATORANIMATOR

ABSOLUTEABSOLUTEOBSERVEROBSERVER

PERSONOIDPERSONOIDANIMATORANIMATOR

WORLDWORLDSIMULATORSIMULATOR

PROTO-PROTO-PERSONOIDPERSONOID

In this image is showed the relations between the world of the In this image is showed the relations between the world of the Agent and the Human Utent. There are distinghished three Agent and the Human Utent. There are distinghished three

different human roles, evidenced in the lighter boxesdifferent human roles, evidenced in the lighter boxes

Definition of the Experiment ArchitectureDefinition of the Experiment Architecture

K

Decomposition of different fields of the Agent

Constrain Environment BodyDomain

World

Animator

Personoid

Animator

Absolute

Observer

I P

To describe the World Simulator and the Proto-Personoid and the To describe the World Simulator and the Proto-Personoid and the interaction between them, will be used the following symbolizationinteraction between them, will be used the following symbolization

The IPK structure is seen from the social prespective according The IPK structure is seen from the social prespective according to the UMP paradigm. Infact in the Domain we can see the to the UMP paradigm. Infact in the Domain we can see the

other different components of the UMP paradigm.other different components of the UMP paradigm.

DOMAINDOMAINSUPERVISORSUPERVISOR

ADVISORADVISOR COOPERATINGCOOPERATINGMANAGERMANAGER

INFORMERINFORMER EXECUTOREXECUTOR

EXPERIMENT: Architecture incrementing EXPERIMENT: Architecture incrementing

IDSS: Intelligent Decision Support SystemsIDSS: Intelligent Decision Support Systems

IDSS:IDSS: “Software program that integrates human intellectual and computer capacities “Software program that integrates human intellectual and computer capacities to improve decision making quality, in semi-structured problems situations”to improve decision making quality, in semi-structured problems situations”

[Keen, [Keen, Scott-MortonScott-Morton, 1996], 1996]

Provides active, partially autonomous Decisional Aid which involve human-like computational intelligence.

Provides passive Informational Aid and Toolkits

IDSSIDSS

DSSDSS

When IDSS is important?When IDSS is important?

• amount of informationamount of information necessary for the management is so large, or its time necessary for the management is so large, or its time density is so high, that the probability of human errors under time constrains is not density is so high, that the probability of human errors under time constrains is not negligible.negligible.

• coping with coping with unexpected situationunexpected situation requires remembering, mental elaboration and requires remembering, mental elaboration and immediate application of complex professional knowledge, which if not properly used, immediate application of complex professional knowledge, which if not properly used, causes fault decisionscauses fault decisions..

Modelling Disaster Domain: Modelling Disaster Domain: Disaster Prop. MapDisaster Prop. Map

Experiment Realization Experiment Realization

We created a general agent, which follows a We created a general agent, which follows a simple set of rules. It represents a first interaction simple set of rules. It represents a first interaction

of the proto-personoid with the external world.of the proto-personoid with the external world.Then, from this generic starting point, we Then, from this generic starting point, we

decompose the various aspects of the agent, decompose the various aspects of the agent, analysing the IPK monad which represent the core analysing the IPK monad which represent the core of the agent. The monad, as we said, is composed of the agent. The monad, as we said, is composed of three different parts (of three different parts (InformationInformation, , PreferencesPreferences

and and KnowledgeKnowledge), and in every new step of our ), and in every new step of our decomposition, we increase the complexity of one decomposition, we increase the complexity of one

of these parts. of these parts. To focus this aspect of the analysis we introduce a To focus this aspect of the analysis we introduce a scale of colours which represent the grade of the scale of colours which represent the grade of the

complexity of the analysed part of the system.complexity of the analysed part of the system.

1

2

3

4

0

5

The main important results of the experiment are:The main important results of the experiment are: modularmodular and and reproduciblereproducible decomposition of the Personoid has been realized. decomposition of the Personoid has been realized. it’s possible to obtain incrementally new specializations of the Personoid focalized on it’s possible to obtain incrementally new specializations of the Personoid focalized on a more detailed problemsa more detailed problems The complexity of the problem ( functionality and architecture) can growth The complexity of the problem ( functionality and architecture) can growth infinitelyinfinitely. .

Proto-Personoids produced in the design experimentProto-Personoids produced in the design experiment

RESULT S OF THE EXPERIMENTRESULT S OF THE EXPERIMENT

Test Case: Disaster DomainTest Case: Disaster Domain

Application of Emergency/Disaster Propagation Application of Emergency/Disaster Propagation FrameworkFramework

Events: Events:

Explosion and fire in chemical factory, Fire in the forest

Emision of toxical substances by tubes to the river

Water in City Aqueduct is toxic

Water users are in danger.

EMERGENCY MANAGER:

Identification of intervention/vulnerable objects, goal of intervention and possible actions

Test Case: Disaster Propagation Map (DPM)Test Case: Disaster Propagation Map (DPM)

TEST Case: Time Diagram without interventionTEST Case: Time Diagram without intervention

PROPAGATION OF EMERGENCY WITHOUT INTERVENTIONPROPAGATION OF EMERGENCY WITHOUT INTERVENTION

Evolution of the DPM without interventionEvolution of the DPM without intervention

Combined together the DPM with the Time Diagram Combined together the DPM with the Time Diagram without intervention, this evolution in time will be obtained without intervention, this evolution in time will be obtained

FactoryFactory

ForestForest

Factory Factory tubestubes

RiverRiver City City AqueductAqueduct

CitizensCitizens

Chicken Chicken FarmFarm

OthersOthers

GEA: IPK Cognitive AgentGEA: IPK Cognitive Agent

Synthesis of the results of the workSynthesis of the results of the work

• Documentation and validation of the TOGA TheoryDocumentation and validation of the TOGA Theory

• 25 Agents prototype realized25 Agents prototype realized

• 30.000 code lines written30.000 code lines written

• GEA prototypeGEA prototype

• User friendly interfaceUser friendly interface

Click here for demonstrationClick here for demonstration

GEA: DemoGEA: Demo

ReferencesReferences

1.1.

2.2.

3. TOGA Meta-theory Web page: 3. TOGA Meta-theory Web page: http://erg4146.casaccia.enea.it/wwwerg26701/Gad-http://erg4146.casaccia.enea.it/wwwerg26701/Gad-toga.htmtoga.htm

4.4.