Ontology-based Multi-agent Systems

by Hadzic, Maja; Wongthongtham, Pornpit; Dillon, Tharam; Chang, Elizabeth

• ISBN13: 9783642019036
• ISBN10: 364201903X
• Format: Hardcover
• Copyright: 2009-08-15
• Publisher: Springer Verlag

Summary

The series Studies in Computational Intelligence (SCI) publishes new developments and advances in the various areas of computational intelligence - quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life science, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems and hybrid intelligent systems. Critical to both contributors and readers are the short publication time and world-wide distribution - this permits a rapid and broad dissemination of research results.

Table of Contents

Current Issues and the Need for Ontologies and Agents	p. 1
Introduction	p. 1
Information Variety	p. 1
Hindrances to Successful Information Retrieval	p. 2
Distributed and Heterogeneous Information without Semantics	p. 3
Underlying Knowledge Base Is Not Available	p. 4
Autonomous, Heterogeneous and Dynamic Information Resources	p. 4
Information Retrieval in Science	p. 4
Search Engines	p. 7
Web Semantics	p. 9
Agents for Dynamic Information Retrieval	p. 11
Ontologies for Intelligent Information Retrieval	p. 12
Conclusion	p. 12
References	p. 12
Introduction to Multi-Agent Systems	p. 15
Introduction	p. 15
Agent Definition	p. 15
Agent's Environment	p. 16
Agent's Characteristics	p. 17
Internal Data Structure of Agents	p. 18
Mobile Agents	p. 19
Dependency Relationships between Agents	p. 22
Agent's Communication	p. 24
Communication Parties	p. 24
Communication Place	p. 24
Communication Time	p. 24
Communication Languages	p. 25
Desired Features of Agent Communication Languages	p. 25
Agent Communication Languages	p. 26
Ontologies for Agent Communication	p. 28
Collaborative Problem-Solving Process in Multi-Agent Systems	p. 29
Different Types of Agents	p. 30
Haag's Classification	p. 30
Dillenbourg et al.'s Agents Classification	p. 31
Maes's Classification	p. 32
Classification According Agent's Functions	p. 33
Conclusion	p. 34
References	p. 35
Introduction to Ontology	p. 37
Introduction	p. 37
Ontology Origins	p. 37
Ontology Definition	p. 38
Ontology Commitments	p. 40
Ontology Community	p. 41
Generalization/Specialization of Ontologies	p. 41
Properties of Ontologies	p. 43
Characteristics of Ontology Models	p. 44
Representation of Ontology Domain	p. 47
Ontology Design Versus Data Modelling	p. 47
Ontology Versus Knowledge Base	p. 49
Classification of Ontologies	p. 51
Degree of Formality	p. 52
Degree of Granularity	p. 53
Level of Generality	p. 53
Amount, Type and Subject of Conceptualization	p. 54
Expressiveness of Ontologies	p. 56
Conclusion	p. 58
References	p. 59
Design Approaches for Multi-Agent-Based Systems	p. 61
Introduction	p. 61
Agent Design Criteria	p. 61
Agent Design Methodologies	p. 62
Belief, Desire and Intention (BDI) Approach	p. 62
Gaia	p. 63
Agent UML	p. 64
Agents in Z	p. 65
Desire	p. 66
Cassiopeia	p. 67
Multi-Agent System Engineering	p. 69
Tropos	p. 70
Prometheus	p. 71
Conclusion	p. 72
References	p. 74
Ontology Design Approaches	p. 75
Introduction	p. 75
Ontology Design Criteria	p. 75
Ontology Design Methodologies	p. 77
Knowledge Engineering Methodology	p. 77
Dogma	p. 82
Tove Methodology	p. 83
Methontology	p. 86
Sensus Methodology	p. 87
Diligent Methodology	p. 89
Conclusion	p. 90
References	p. 90
Significance of Ontologies, Agents and Their Integration	p. 93
Introduction	p. 93
Advantages of Ontologies	p. 93
Ontologies for Data Semantics	p. 93
Ontologies as Basis for Knowledge Sharing	p. 94
Ontologies as Basis for Knowledge Representation	p. 98
Ontologies as Basis for Knowledge Management	p. 98
Ontologies for Intelligent Information Retrieval	p. 99
Ontologies for Mediation	p. 99
Ontologies for Natural Language Applications	p. 100
Advantages of Agent-Based Systems	p. 101
Agents are Autonomous	p. 102
Agents Support Computational Intelligence	p. 103
Distributed Mobile Agent-Based Computing	p. 103
Agents Collaboration and Cooperation in Their Activities	p. 105
Agents Support Automated Service Discovery	p. 105
Ontology and Agent-Based Systems Complementing Each Other	p. 106
Problem Decomposition	p. 106
Locating and Retrieving of Information	p. 107
Agent Communication	p. 107
Information Analysis and Manipulation	p. 108
Conclusion	p. 109
References	p. 109
Design Methodology for Integrated Systems - Part I (Ontology Design)	p. 111
Introduction	p. 111
Generalization and Conceptualization of the Domain	p. 111
Ontology Communities	p. 111
Purpose of the Ontology	p. 112
Ontology Domain	p. 113
Application of the Ontology	p. 114
Aligning and Merging Ontologies	p. 115
Identify Suitable Ontologies for Reuse	p. 115
Define Merging and Alignment Tool	p. 116
Import the Source Ontologies	p. 116
Identify Ontology Correspondences	p. 117
Align and/or Merge Ontologies	p. 117
Formal Specification of Conceptualization	p. 118
Ontology Concepts	p. 119
Relationships between Concepts	p. 119
Groups of Related Concepts	p. 119
Formal Specification of Ontology Commitments	p. 120
Identify the Ontology Commitments	p. 120
Formalize the Commitments	p. 121
Identify Reusable Knowledge Components	p. 121
Ontology Evaluation	p. 122
Ontology Design Quality Evaluation	p. 122
Ontology Usability Evaluation	p. 123
Overview of the Ontology Development	p. 123
Conclusion	p. 125
References	p. 126
Design Methodology for Integrated Systems - Part II (Multi-Agent System Design)	p. 127
Introduction	p. 127
Overview of the Agent Methodology	p. 127
Classification of Agents According to Their Responsibilities	p. 128
Establish Intuitive Flow of Problem Solving, Task and Result Sharing	p. 129
Identify Corresponding Agent Functions	p. 129
Identify Corresponding Agent Types	p. 130
Identify the Need for an Ontology to Support Agent's Intelligence	p. 131
Problem Decomposition	p. 131
Information Retrieval	p. 132
Agent Communication	p. 132
Information Analysis and Manipulation	p. 132
Meaningful Information Presentation	p. 133
Define Agent's Collaborations	p. 133
Establish Efficient Organization of Agents	p. 134
Establish Correspondence between Agent Types and Their Organization	p. 134
Construction of Individual Agents	p. 135
Identify Required Agent Components	p. 136
Construct Various Agents	p. 136
Protect the System by Implementing Security Requirements	p. 137
Identify Security Requirements	p. 137
Implement the Requirements	p. 138
Conclusion	p. 140
Advantages of the Onto-Agents Methodology	p. 141
References	p. 142
Notations for the Integrated Ontology and Multi-Agent System Design	p. 143
Introduction	p. 143
Modelling Notations	p. 144
Ontology Modelling Notations	p. 144
Class Notation	p. 148
Generalisation Notation	p. 149
Property Notation	p. 150
Restriction Notation	p. 154
Associated Class Notation	p. 155
Ontology Instances Notation	p. 156
Agent Modelling Notations	p. 157
Role Notation	p. 158
Communication Notation	p. 158
Agent Notation	p. 159
Diagrams	p. 160
Conclusion	p. 163
References	p. 163
Architecture of the Integrated Ontology and Multi-Agent System	p. 165
Introduction	p. 165
Ontology Server	p. 166
Ontology Repositories	p. 169
Ontology Evolution	p. 170
Ontology Management	p. 173
Agent Platform	p. 173
Conclusion	p. 177
References	p. 177
Case Study I: Ontology-Based Multi-Agent System for Human Disease Studies	p. 179
Introduction	p. 179
Generalization and Conceptualization of the Medical Domain	p. 179
Community Associated with the Ontology	p. 180
Purpose of the Ontology	p. 180
Ontology Domain	p. 181
Application Based on the Designed Ontology	p. 182
Top-Level Hierarchy of GHDO	p. 182
Aligning and Merging Existing Medical Ontologies	p. 184
Identifying Ontologies Suitable to Be Reused	p. 184
Define Alignment and Merge Tool	p. 184
Import Source Ontologies	p. 185
Identify Correspondences between Different Ontologies	p. 185
Align and Merge Ontologies	p. 185
Formal Specification of Human Disease Domain Conceptualization	p. 185
Ontology Concepts	p. 186
Relationships between Concepts	p. 186
Lexons	p. 186
Relationships between Lexons	p. 187
Groups of Related Lexons	p. 188
Formal Specification of Human Disease Ontology Commitments	p. 189
Identify Intra- and Inter-Commitments	p. 189
Formalize the Ontology Commitments	p. 190
Identify Reusable Knowledge Components	p. 190
Human Disease Ontology Evaluation	p. 191
Classification of Agents According to their Responsibilities within the Human Disease Information Retrieval System	p. 191
Establish Intuitive Flow of Problem Solving, Task and Result Sharing	p. 191
Identify Corresponding Agent Functions	p. 191
Identify Corresponding Agent Types	p. 191
Identify the Need for Human Disease Ontology to Support Agents' Intelligence	p. 193
Problem Decomposition and Task Assignments	p. 193
Information Retrieval	p. 195
Agent Communication	p. 195
Information Analysis and Manipulation	p. 197
Meaningful Information Presentation	p. 198
Define Agent's Collaboration within the Intelligent Human Disease Information Retrieval System	p. 198
Establish Efficient Organization of Agents	p. 198
Establish Correspondence between Agent Types and Their Organization	p. 199
Query Processing and Information Integration within GHMS	p. 200
Construction of Individual Agents of the Human Disease Information Retrieval System	p. 202
Identify Required Agents' Components	p. 202
Construct Various Agents	p. 205
Protect the Human Disease Information Retrieval System by Implementing Security Requirements	p. 205
Identify Security Requirements	p. 205
Implement the Security Requirements	p. 205
Examples of Use of the Intelligent Human Disease Information Retrieval System	p. 207
Example 1: Help Physician to Identify Disease	p. 208
Example 2: Support Physician to Choose Disease Treatments	p. 210
Example 3: Help Patients and General Public to Prevent a Disease	p. 210
Example 4: Help Medical Researchers to Identify Disease Causes	p. 211
Example 5: Help Medical Researcher Study Complex Diseases	p. 213
Conclusion	p. 215
References	p. 215
Case Study II: Ontology-Based Multi-Agent System for Software Engineering Studies	p. 217
Introduction	p. 217
Generalization and Conceptualisation of the Software Engineering Domain	p. 217
Purpose of the Software Engineering Ontology	p. 217
The Context of Software Engineering Domain Knowledge	p. 218
Community	p. 221
Application Based on the Software Engineering Ontology	p. 223
Formal Specifications of Software Engineering Domain Conceptualisation	p. 234
SE Ontology Concepts	p. 234
SE Ontology Relations and Constraints	p. 235
SE Ontology Instances	p. 240
SE Ontology Validation	p. 243
Validation of Communications	p. 243
Validation of Knowledge Sharing	p. 246
Validation of Knowledge Management	p. 250
Classificaiton of Agents	p. 256
Need of the Software Engineering Ontology to Support Agent's Intelligence	p. 257
Agent's Collaborations	p. 257
Construction of Individual Agents	p. 261
User Agents	p. 261
Safeguard Agents	p. 262
Ontology Agents	p. 262
Decision Making Agents	p. 263
Practical Uses	p. 264
Conclusion	p. 269
References	p. 269
Potential Applications of Ontology-Based Multi-Agent Systems	p. 271
Overview	p. 271
Collaborative Environments	p. 271
Information Access and Retrieval	p. 272
Data Mining	p. 272
Open Issues	p. 273
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