Presentation on theme: "Top-Down Incremental Development of Agents' Architecture for Emergency Management Systems: Management Systems: TOGA methodology HID, CAMO Seminars Series."— Presentation transcript:
Top-Down Incremental Development of Agents' Architecture for Emergency Management Systems: Management Systems: TOGA methodology HID, CAMO Seminars Series This activity is realized in cooperation between La Sapienza University and ENEA: F.Delli Priscoli (Univ. La Sapienza, Rome), A.M.Gadomski (CAMO, ENEA), A.Caputo - thesis (Univ. La Sapienza - Engineering Dep., ENEA scholarship 2002/0362) Andrea Caputo, Adam Maria Gadomski, Franco Delli Priscoli May 2005 University of Rome La SapienzaItalian National Research Agency ENEA
Top-Down Incremental Development of Intelligent Agents' Architecture Intelligent Agents' Architecture: Problem Specification Intelligent Agents' Architecture: Problem Specification Existing Design & Programming styles (short soa) Existing Design & Programming styles (short soa) TOGA Theoretical Tool TOGA Theoretical Tool Method: Top-Down incremental development Method: Top-Down incremental development Emergency Management Test-Case Emergency Management Test-Case Conclusions Conclusions Prototype demonstration Prototype demonstration Presentation outline
Contents of the Caputos Thesis General request overiview General request overiview Contest of the simulation: Socio-Cognitive Engineering Contest of the simulation: Socio-Cognitive Engineering A TOGA proposal A TOGA proposal IPK monad IPK monad Universal Management Paradigms Universal Management Paradigms Example showed at SCEF 2003 Example showed at SCEF 2003 Intelligent Decision Support System Intelligent Decision Support System Modelling Disaster Domain Modelling Disaster Domain Disaster Propagation Disaster Propagation GEA GEA
NaturalSciences SoftwareTechnology ArtificialIntelligence Socio-CognitiveEngineering Contest of the Simulation From the Socio-cognitive contest we will arrive at a ripetitive, incremental, ricorsive, distribuite INTELLIGENT ENTITY [ 1 ]
IPK Informations( I ) Informations( I ) Preferences( P ) Preferences( P ) Knowledges( K ) Knowledges( K ) I = K x I I, I DD K x K K x = P s (K, I) UMP Universal Management Paradigm (UMP) is a functional architecture of organizational High-Intelligence for every natural and artificial High- Intelligent agents organization. It is characterized by: Complete Complete Relative Relative Recursive Recursive Incremental Incremental IPK paradigm and UMP describe essential functional properties of abstract highly intelligent entities, natural and artificial. A TOGA PROPOSAL [ 2 ] I KP SOCIO-COGNITIVE ENGINEERING PARADIGMS
structural assumptions: structural assumptions: -- Recursivity -- Recursivity -- Iterativness -- Iterativness -- Repetitivity -- Repetitivity -- Modularity -- Modularity They intend to minimize total axiomatic information employed by the theory. They intend to minimize total axiomatic information employed by the theory. methodological assumptions, which require completeness and congruence methodological assumptions, which require completeness and congruence of the problem conceptualization on every abstraction level. of the problem conceptualization on every abstraction level. terminological assumption, to reduce the number of terms as is possible. terminological assumption, to reduce the number of terms as is possible. The key TOGA paradigms (top assumptions/axioms) are divided on [ 3 ] : Conceptualization, Ontological, and Methodological TOGA Normative Meta-Assumptions
Three components: TAO : Basic conceptualization frame independent on represented domain of interest. independent on represented domain of interest. KNOCS : Axioms system for the real-world problem representation MRUS : Methodological RUles Systems Non ordered observations, knowledge, values TAO Conceptualizations KNOCS Conceptualization Goal-oriented Problem Model MRUS: Methodological Rules System They refers to an Abstract Intelligent Agent (AIA), his/her/its Domain-of-Activity and to the relations between them. Summarizing, 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 Framework
Personois: IPK Abstract Agent Model Axioms Model Axioms Repetivety Repetivety Modularity Modularity Recursivity Recursivity … I P K PK PK I LEVEL I I II META-LEVEL
Disaster Manager: simple model example InInInIn KP I1I1I1I1 KP I2I2I2I2 KP I3I3I3I3 KP Infrastructure Network Real Emergency Domain I KP Agent Manager Agent 1 Agent 2 Agent 3 Agent n I : Information P : Preferences K : Knowledge
Objectives of experiment: why? Practical vefification of the methodology by the designing a series of agents with incremental 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 evolution line of the incremental design of Intelligent Agents aimed at the development of the model of an Intelligent Entity The representation of the abstract world of the Agent is: WORLDANIMATOR ABSOLUTEOBSERVER PERSONOIDANIMATORWORLDSIMULATORPROTO-PERSONOID In this image is showed the relations between the world of the Agent and the Human Utent. There are distinghished three different human roles, evidenced in the lighter boxes Definition of the Experiment Architecture
K Decomposition of different fields of the Agent Constrain Environment BodyDomain World Animator Personoid Animator Absolute Observer IP To describe the World Simulator and the Proto-Personoid and the interaction between them, will be used the following symbolization The IPK structure is seen from the social prespective according to the UMP paradigm. Infact in the Domain we can see the other different components of the UMP paradigm. DOMAIN SUPERVISOR ADVISORCOOPERATINGMANAGER INFORMEREXECUTOR EXPERIMENT: Architecture incrementing
IDSS: Intelligent Decision Support Systems IDSS: Software program that integrates human intellectual and computer capacities to improve decision making quality, in semi-structured problems situations [Keen, Scott-Morton, 1996] to improve decision making quality, in semi-structured problems situations [Keen, Scott-Morton, 1996] Provides active, partially autonomous Decisional Aid which involve human-like computational intelligence. Provides passive Informational Aid and Toolkits IDSS DSS When IDSS is important? amount of information 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 negligible. amount of information 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 negligible. coping with unexpected situation requires remembering, mental elaboration and immediate application of complex professional knowledge, which if not properly used, causes fault decisions. coping with unexpected situation requires remembering, mental elaboration and immediate application of complex professional knowledge, which if not properly used, causes fault decisions.
Modelling Disaster Domain: Disaster Prop. Map
Experiment Realization We created a general agent, which follows a simple set of rules. It represents a first interaction of the proto-personoid with the external world. Then, from this generic starting point, we decompose the various aspects of the agent, analysing the IPK monad which represent the core of the agent. The monad, as we said, is composed of three different parts (Information, Preferences and Knowledge), and in every new step of our decomposition, we increase the complexity of one of these parts. To focus this aspect of the analysis we introduce a scale of colours which represent the grade of the complexity of the analysed part of the system.
The main important results of the experiment are: modular and reproducible decomposition of the Personoid has been realized. modular and reproducible decomposition of the Personoid has been realized. its possible to obtain incrementally new specializations of the Personoid focalized on a more detailed problems its possible to obtain incrementally new specializations of the Personoid focalized on a more detailed problems The complexity of the problem ( functionality and architecture) can growth infinitely. The complexity of the problem ( functionality and architecture) can growth infinitely. Proto-Personoids produced in the design experiment RESULT S OF THE EXPERIMENT
Test Case: Disaster Domain Application of Emergency/Disaster Propagation Framework 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: Time Diagram without intervention PROPAGATION OF EMERGENCY WITHOUT INTERVENTION
Evolution of the DPM without intervention Combined together the DPM with the Time Diagram without intervention, this evolution in time will be obtained Factory Forest Factory tubes River City Aqueduct Citizens Chicken Farm Others
GEA: IPK Cognitive Agent
Synthesis of the results of the work Documentation and validation of the TOGA Theory Documentation and validation of the TOGA Theory 25 Agents prototype realized 25 Agents prototype realized code lines written code lines written GEA prototype GEA prototype User friendly interface User friendly interface
Click hereClick here for demonstration Click here GEA: Demo
References TOGA Meta-theory Web page: toga.htm 4.