Upload
angelo
View
20
Download
0
Embed Size (px)
DESCRIPTION
HID, CAMO Seminars Series. Top-Down Incremental Development of Agents' Architecture for Emergency Management Systems: TOGA methodology. Andrea Caputo, Adam Maria Gadomski, Franco Delli Priscoli May 2005. University of Rome “La Sapienza”. Italian National Research Agency ENEA. - PowerPoint PPT Presentation
Citation preview
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 DDDKKxx K KKKxx = = PPss (K, I) (K, I)
UMPUMPUniversal Management ParadigmUniversal Management Paradigm(UMP) is a (UMP) is a functional architecturefunctional architectureof organizational High-Intelligenceof organizational High-Intelligencefor 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 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
IDSSIDSSDSSDSS
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.