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Semantic Knowledge Model and Architecture for Agents in Discrete Environments Michal Laclavík Ústav informatiky Slovenskej akadémie vied

Semantic Knowledge Model and Architecture for Agents in Discrete Environments

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Semantic Knowledge Model and Architecture for Agents in Discrete Environments. Michal Laclav í k Ústav informatiky Slovenskej akadémie vied. Outline. Motivation State of the Art Objectives Methodology and Tools Agent Knowledge Model – Models, Methodology, Library Applications Conclusion. - PowerPoint PPT Presentation

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Page 1: Semantic Knowledge Model and Architecture for Agents in Discrete Environments

Semantic Knowledge Model and Architecture for Agents in Discrete Environments

Michal LaclavíkÚstav informatikySlovenskej akadémie vied

Page 2: Semantic Knowledge Model and Architecture for Agents in Discrete Environments

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Outline Motivation State of the Art Objectives Methodology and Tools Agent Knowledge Model – Models,

Methodology, Library Applications Conclusion

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Motivation and State of the Art

MAS is powerful paradigm for distributed or heterogeneous systems

MAS need Knowledge Support and Semantics MAS need Connection with Existing Commercial Standards

Agent Technology Roadmap: Current MAS Systems – lack of Internal Agent Knowledge Model, lack of interconnection with semantic web results (knowledge model representations) and commercial standards

Focus on Agents and Knowledge representation (Ontologies) Knowledge Management and Experience Management as

application domains

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State of the Art - Agents

Agent Definition: An agent is a computer system capable of flexible autonomous action in a dynamic, unpredictable and open environment. (LUCK 2003)

MAS Standards: FIPA, MASIF Related to agent communication, agent platforms No standards for internal agent knowledge model

with available implementations

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State of the Art - Agents Architectures:

Reactive Architecture No specification of knowledge model, behavior of agent is

based on implemented responses to environment states Belief Desire Intention Architecture – BDI

Belief – represents knowledge model, available some implementations based on logic programming, not used in FIPA compliant MAS

Behavioral Architecture FIPA compliant MAS are based on such architecture No specification of Internal Agent Knowledge model – depend

on agent designer and developer JADE Agent System

Support for ontologies based on FIPA-SL (Similar to First Order Logic)

No Query engine No Storage No Inference

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State of the Art – Ontologies,

Knowledge

Ontologies Knowledge Representation OWL-DL compatible with Description

Logic Query and Storage Engines

available RDF, OWL, RDQL based

Application domain Knowledge Management (KM) is

the process through which organizations generate value from their intellectual and knowledge-based assets (Source: CIO Magazine)

Experience Management is special kind of KM – based on “lessons learned”

Characters

Data

Information

Knowledge

Actions

Syntax

Semantics

Pragmatics

Reasoning

(Bergman, 2002, Experience Management)

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Problem Specification

Multi Agent System

Agent 1

Agent 2

Agent 3

Graphical User Interface

External System

Knowledge Base

DirectoryFacilitator

Knowledge StorageQuerying

XML, XML-RPC, SOAP

User requestsDisplaying results

FIPA ACL, KIF, FIPA-SL, FIPA-RDF

FIPA ACL,RDF/OWL, RDQL

Knowledge Model

KM

KM

IIOP, HTTP, SMTP

ACL

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State of The Art Conclusion Focus on software, intelligent and FIPA

compliant agents Providing better semantic

infrastructure (ontologies, knowledge models)

Apply basic principles of software and knowledge engineering

Make stronger connection between MAS and existing commercial technologies

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Used Tools and Software

Protégé Ontology Editor Support for OWL ontology format Can be used as modeling tool

JADE (Java Agent DEvelopment Framework) Most developing MAS framework Compliant with FIPA standards

Jena – Semantic Web Framework for Java Support for OWL – best available OWL API Support for RDQL model querying

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Agent Knowledge Model

Objective:Design of Agent Architecture using Ontology based Knowledge Model

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Agent Knowledge Model

Based on Events, Resources, Actions, Actors, Context

Formally Described using Sets, Description Logic (compatible with OWL-DL), Graph Representation

Actor Context updating function/algorithm (Actor Environment State)

CAnew = fC(ea,CA

old) Resources updating function/algorithm

(result of fulfilled actor goals) RA

new = fR(CAnew,RA

old)

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Software Development Methodology

Objective:Design of Software Development Methodology for creation of Agents with Ontology based Knowledge Model

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Development Methodology (Knowledge Model)

Extending Model with Protégé Editor following CommonKADS models

Organizational or Environment Model Task Model Agent or Actor Model

Includes implementation of algorithms for context and resource updating

Results Ontology developed in Protégé which can be

exported in OWL format. Concrete Algorithms for each actor (often algorithms

are similar or same) which updates actors' context CA

new and resources RAnew.

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Development Methodology (System Design)

UML Diagrams for concrete Application Domain

Use Case Diagram for each agent agent is taken as system

boundaries Sequence Diagram

Communication among agents

Class Diagram Behaviors are described as methods

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Agent Software Library

Objectives:

Design of Agent Architecture using Ontology based Knowledge Model

Design & Development of Software Library for building Intelligent Agents with Ontology Knowledge Model with possibility to plug agents to existing commercial technologies

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Agent Software Library Support for OWL based Agent Knowledge Model Support for XML-RPC connection to receive

event and send plain XML Support for agent communication using FIPA

ACL with OWL and RDQL as content languages Support for Presentation of Ontological

Knowledge (RDF/OWL => plain XML + XSL => HTML)

JADE and Jena Integration Available on JADE official website

to MAS community

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Agent Library Example

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Support for Knowledge and Experience Management

Objective:Design of Generic Ontology Model for Experience Management with extension to different application domains.

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Extension of Model for Experience Management

Extended Agent Memory Model

Workflow Related WfInstance, WfActivity

ActiveHint Sub class of resource Representation of

Experience Employee

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Presentation of Ontology based Knowledge

Objective:Design and Development of user friendly Knowledge Presentation for MAS.

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Presentation of Ontology based Knowledge

Ontology Tree Browse window

Graph XSL Transformation

RDF/OWL => Plain XML + XSL => HTML

Infrastructure to receive plain XML using XML-RPC

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Applications

Objective:Evaluation of Results on real pilot operation.

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Pellucid 5FP IST Project Title: Platform for

Organizationally Mobile Public Employees

Duration: Sep 2002- Dec 2004 Knowledge Management to

support employees Workflow based Administration

Processes To support Employee Mobility

in organization Agent Architecture based on

autonomous co-operating agents

Process Layer

Interaction Layer

Pellucid Architecture

Pellucid Agents

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Pellucid Applications

CDG, Genoa, ItalyTraffic Light Management

MMBG, Sanlucar, SpainProject Management

SADESI, Seville, SpainTelephone Incidence Resolution

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K-Wf Grid 6FP IST Project

Work on new EMBET architecture Current state: User Assistant Agent in K-Wf Grid uses

model presented in thesis. Algorithms presented in chapter 5 were reused with

same improvements and modifications. Architecture is not Agent based but users of system

are modeled as actors. Knowledge Model, its implementation and

modified algorithms presented in thesis are used

Title: Knowledge-based Workflow System for Grid Applications

Objectives: To support workflow construction and execution with Knowledge

Duration: Sep 2004 - Feb 2007

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Conclusion and Future Work

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Conclusion (1)

The most significant scientific achievements Agent knowledge model

Applicable in any discrete environment where actors need to be modeled

Can be expressed by ontology, sets or description logic Such model was found useful for:

Simple goal oriented agents Knowledge Management Solution based on Agents

(Pellucid) Experience Management Solution non agent based

(EMBET System) Development Methodology

Speed up Knowledge based Agent development for concrete application domains

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Conclusion (2) The most significant development achievements

Agent Library Support for OWL based Agent Knowledge Model Support for XML-RPC connection to receive event and send

plain XML Support for Presentation of Ontological Knowledge (RDF/OWL

=> plain XML + XSL => HTML) Support for agent communication using FIPA ACL with OWL

and RDQL as content languages JADE and Jena Integration Available on JADE official website to MAS community

(year 2005 – 577 downloads)

Page 29: Semantic Knowledge Model and Architecture for Agents in Discrete Environments

Thank you !

Michal Laclavik