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Ontology-Based Multi-Agent Systems 2009 ed. [Kõva köide]

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  • Formaat: Hardback, 274 pages, kõrgus x laius: 235x155 mm, kaal: 1280 g, XIV, 274 p., 1 Hardback
  • Sari: Studies in Computational Intelligence 219
  • Ilmumisaeg: 25-Jun-2009
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 364201903X
  • ISBN-13: 9783642019036
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Ontology-Based Multi-Agent Systems 2009 ed.Suurem pilt
  • Formaat: Hardback, 274 pages, kõrgus x laius: 235x155 mm, kaal: 1280 g, XIV, 274 p., 1 Hardback
  • Sari: Studies in Computational Intelligence 219
  • Ilmumisaeg: 25-Jun-2009
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 364201903X
  • ISBN-13: 9783642019036
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783642019036.html
Märksõnad:
During the last two decades, the idea of Semantic Web has received a great deal of attention. An extensive body of knowledge has emerged to describe technologies that seek to help us create and use aspects of the Semantic Web. Ontology and agent-based technologies are understood to be the two important technologies here. A large number of articles and a number of books exist to describe the use individually of the two technologies and the design of systems that use each of these technologies individually, but little focus has been given on how one can - sign systems that carryout integrated use of the two different technologies. In this book we describe ontology and agent-based systems individually, and highlight advantages of integration of the two different and complementary te- nologies. We also present a methodology that will guide us in the design of the - tegrated ontology-based multi-agent systems and illustrate this methodology on two use cases from the health and software engineering domain. This book is organized as follows: Chapter I, Current issues and the need for ontologies and agents, describes existing problems associated with uncontrollable information overload and explains how ontologies and agent-based systems can help address these - sues. Chapter II, Introduction to multi-agent systems, defines agents and their main characteristics and features including mobility, communications and collaboration between different agents. It also presents different types of agents on the basis of classifications done by different authors.
1 Current Issues and the Need for Ontologies and Agents
1
1.1 Introduction
1
1.2 Information Variety
1
1.3 Hindrances to Successful Information Retrieval
2
1.3.1 Distributed and Heterogeneous Information without Semantics
3
1.3.2 Underlying Knowledge Base Is Not Available
4
1.3.3 Autonomous, Heterogeneous and Dynamic Information Resources
4
1.4 Information Retrieval in Science
4
1.5 Search Engines
7
1.6 Web Semantics
9
1.7 Agents for Dynamic Information Retrieval
11
1.8 Ontologies for Intelligent Information Retrieval
12
1.9 Conclusion
12
References
12
2 Introduction to Multi-Agent Systems
15
2.1 Introduction
15
2.2 Agent Definition
15
2.3 Agent's Environment
16
2.4 Agent's Characteristics
17
2.5 Internal Data Structure of Agents
18
2.6 Mobile Agents
19
2.7 Dependency Relationships between Agents
22
2.8 Agent's Communication
24
2.8.1 Communication Parties
24
2.8.2 Communication Place
24
2.8.3 Communication Time
24
2.8.4 Communication Languages
25
2.8.4.1 Desired Features of Agent Communication Languages
25
2.8.4.2 Agent Communication Languages
26
2.8.4.3 Ontologies for Agent Communication
28
2.9 Collaborative Problem-Solving Process in Multi-Agent Systems
29
2.10 Different Types of Agents
30
2.10.1 Haag's Classification
30
2.10.2 Dillenbourg et al.'s Agents Classification
31
2.10.3 Maes's Classification
32
2.10.4 Classification According Agent's Functions
33
2.11 Conclusion
34
References
35
3 Introduction to Ontology
37
3.1 Introduction
37
3.2 Ontology Origins
37
3.3 Ontology Definition
38
3.4 Ontology Commitments
40
3.5 Ontology Community
41
3.6 Generalization/Specialization of Ontologies
41
3.7 Properties of Ontologies
43
3.8 Characteristics of Ontology Models
44
3.9 Representation of Ontology Domain
47
3.10 Ontology Design Versus Data Modelling
47
3.11 Ontology Versus Knowledge Base
49
3.12 Classification of Ontologies
51
3.12.1 Degree of Formality
52
3.12.2 Degree of Granularity
53
3.12.3 Level of Generality
53
3.12.4 Amount, Type and Subject of Conceptualization
54
3.12.5 Expressiveness of Ontologies
56
3.13 Conclusion
58
References
59
4 Design Approaches for Multi-Agent-Based Systems
61
4.1 Introduction
61
4.2 Agent Design Criteria
61
4.3 Agent Design Methodologies
62
4.3.1 Belief, Desire and Intention (BDI) Approach
62
4.3.2 Gaia
63
4.3.3 Agent UML
64
4.3.4 Agents in Z
65
4.3.5 DESIRE
66
4.3.6 Cassiopeia
67
4.3.7 Multi-Agent System Engineering
69
4.3.8 TROPOS
70
4.3.9 Prometheus
71
4.4 Conclusion
72
References
74
5 Ontology Design Approaches
75
5.1 Introduction
75
5.2 Ontology Design Criteria
75
5.3 Ontology Design Methodologies
77
5.3.1 Knowledge Engineering Methodology
77
5.3.2 DOGMA
82
5.3.3 TOVE Methodology
83
5.3.4 METHONTOLOGY
86
5.3.5 SENSUS Methodology
87
5.3.6 DILIGENT Methodology
89
5.4 Conclusion
90
References
90
6 Significance of Ontologies, Agents and Their Integration
93
6.1 Introduction
93
6.2 Advantages of Ontologies
93
6.2.1 Ontologies for Data Semantics
93
6.2.2 Ontologies as Basis for Knowledge Sharing
94
6.2.3 Ontologies as Basis for Knowledge Representation
98
6.2.4 Ontologies as Basis for Knowledge Management
98
6.2.5 Ontologies for Intelligent Information Retrieval
99
6.2.6 Ontologies for Mediation
99
6.2.7 Ontologies for Natural Language Applications
100
6.3 Advantages of Agent-Based Systems
101
6.3.1 Agents are Autonomous
102
6.3.2 Agents Support Computational Intelligence
103
6.3.3 Distributed Mobile Agent-Based Computing
103
6.3.4 Agents Collaboration and Cooperation in Their Activities
105
6.3.5 Agents Support Automated Service Discovery
105
6.4 Ontology and Agent-Based Systems Complementing Each Other
106
6.4.1 Problem Decomposition
106
6.4.2 Locating and Retrieving of Information
107
6.4.3 Agent Communication
107
6.4.4 Information Analysis and Manipulation
108
6.5 Conclusion
109
References
109
7 Design Methodology for Integrated Systems - Part I (Ontology Design)
111
7.1 Introduction
111
7.2 Generalization and Conceptualization of the Domain
111
7.2.1 Ontology Communities
111
7.2.2 Purpose of the Ontology
112
7.2.3 Ontology Domain
113
7.2.4 Application of the Ontology
114
7.3 Aligning and Merging Ontologies
115
7.3.1 Identify Suitable Ontologies for Reuse
115
7.3.2 Define Merging and Alignment Tool
116
7.3.3 Import the Source Ontologies
116
7.3.4 Identify Ontology Correspondences
117
7.3.5 Align and/or Merge Ontologies
117
7.4 Formal Specification of Conceptualization
118
7.4.1 Ontology Concepts
119
7.4.2 Relationships between Concepts
119
7.4.3 Groups of Related Concepts
119
7.5 Formal Specification of Ontology Commitments
120
7.5.1 Identify the Ontology Commitments
120
7.5.2 Formalize the Commitments
121
7.5.3 Identify Reusable Knowledge Components
121
7.6 Ontology Evaluation
122
7.6.1 Ontology Design Quality Evaluation
122
7.6.2 Ontology Usability Evaluation
123
7.7 Overview of the Ontology Development
123
7.8 Conclusion
125
References
126
8 Design Methodology for Integrated Systems - Part II (Multi-Agent System Design)
127
8.1 Introduction
127
8.2 Overview of the Agent Methodology
127
8.3 Classification of Agents According to Their Responsibilities
128
8.3.1 Establish Intuitive Flow of Problem Solving, Task and Result Sharing
129
8.3.2 Identify Corresponding Agent Functions
129
8.3.3 Identify Corresponding Agent Types
130
8.4 Identify the Need for an Ontology to Support Agent's Intelligence
131
8.4.1 Problem Decomposition
131
8.4.2 Information Retrieval
132
8.4.3 Agent Communication
132
8.4.4 Information Analysis and Manipulation
132
8.4.5 Meaningful Information Presentation
133
8.5 Define Agent's Collaborations
133
8.5.1 Establish Efficient Organization of Agents
134
8.5.2 Establish Correspondence between Agent Types and Their Organization
134
8.6 Construction of Individual Agents
135
8.6.1 Identify Required Agent Components
136
8.6.2 Construct Various Agents
136
8.7 Protect the System by Implementing Security Requirements
137
8.7.1 Identify Security Requirements
137
8.7.2 Implement the Requirements
138
8.8 Conclusion
140
8.9 Advantages of the Onto-Agents Methodology
141
References
142
9 Notations for the Integrated Ontology and Multi-Agent System Design
143
9.1 Introduction
143
9.2 Modelling Notations
144
9.2.1 Ontology Modelling Notations
144
9.2.1.1 Class Notation
148
9.2.1.2 Generalisation Notation
149
9.2.1.3 Property Notation
150
9.2.1.4 Restriction Notation
154
9.2.1.5 Associated Class Notation
155
9.2.1.6 Ontology Instances Notation
156
9.2.2 Agent Modelling Notations
157
9.2.2.1 Role Notation
158
9.2.2.2 Communication Notation
158
9.2.2.3 Agent Notation
159
9.3 Diagrams
160
9.4 Conclusion
163
References
163
10 Architecture of the Integrated Ontology and Multi-Agent System 165
10.1 Introduction
165
10.2 Ontology Server
166
10.2.1 Ontology Repositories
169
10.2.2 Ontology Evolution
170
10.2.3 Ontology Management
173
10.3 Agent Platform
173
10.4 Conclusion
177
References
177
11 Case Study I: Ontology-Based Multi-Agent System for Human Disease Studies 179
11.1 Introduction
179
11.2 Generalization and Conceptualization of the Medical Domain
179
11.2.1 Community Associated with the Ontology
180
11.2.2 Purpose of the Ontology
180
11.2.3 Ontology Domain
181
11.2.4 Application Based on the Designed Ontology
182
11.2.5 Top-Level Hierarchy of GHDO
182
11.3 Aligning and Merging Existing Medical Ontologies
184
11.3.1 Identifying Ontologies Suitable to Be Reused
184
11.3.2 Define Alignment and Merge Tool
184
11.3.3 Import Source Ontologies
185
11.3.4 Identify Correspondences between Different Ontologies
185
11.3.5 Align and Merge Ontologies
185
11.4 Formal Specification of Human Disease Domain Conceptualization
185
11.4.1 Ontology Concepts
186
11.4.2 Relationships between Concepts
186
11.4.3 Lexons
186
11.4.4 Relationships between Lexons
187
11.4.5 Groups of Related Lexons
188
11.5 Formal Specification of Human Disease Ontology Commitments
189
11.5.1 Identify Intra- and Inter-Commitments
189
11.5.2 Formalize the Ontology Commitments
190
11.5.3 Identify Reusable Knowledge Components
190
11.6 Human Disease Ontology Evaluation
191
11.7 Classification of Agents According to their Responsibilities within the Human Disease Information Retrieval System
191
11.7.1 Establish Intuitive Flow of Problem Solving, Task and Result Sharing
191
11.7.2 Identify Corresponding Agent Functions
191
11.7.3 Identify Corresponding Agent Types
191
11.8 Identify the Need for Human Disease Ontology to Support Agents' Intelligence
193
11.8.1 Problem Decomposition and Task Assignments
193
11.8.2 Information Retrieval
195
11.8.3 Agent Communication
195
11.8.4 Information Analysis and Manipulation
197
11.8.5 Meaningful Information Presentation
198
11.9 Define Agent's Collaboration within the Intelligent Human Disease Information Retrieval System
198
11.9.1 Establish Efficient Organization of Agents
198
11.9.2 Establish Correspondence between Agent Types and Their Organization
199
11.9.3 Query Processing and Information Integration within GHMS
200
11.10 Construction of Individual Agents of the Human Disease Information Retrieval System
202
11.10.1 Identify Required Agents' Components
202
11.10.2 Construct Various Agents
205
11.11 Protect the Human Disease Information Retrieval System by Implementing Security Requirements
205
11.11.1 Identify Security Requirements
205
11.11.2 Implement the Security Requirements
205
11.12 Examples of Use of the Intelligent Human Disease Information Retrieval System
207
11.12.1 Example 1: Help Physician to Identify Disease
208
11.12.2 Example 2: Support Physician to Choose Disease Treatments
210
11.12.3 Example 3: Help Patients and General Public to Prevent a Disease
210
11.12.4 Example 4: Help Medical Researchers to Identify Disease Causes
211
11.12.5 Example 5: Help Medical Researcher Study Complex Diseases
213
11.13 Conclusion
215
References
215
12 CASE STUDY II: Ontology-Based Multi-Agent System for Software Engineering Studies 217
12.1 Introduction
217
12.2 Generalization and Conceptualisation of the Software Engineering Domain
217
12.2.1 Purpose of the Software Engineering Ontology
217
12.2.2 The Context of Software Engineering Domain Knowledge
218
12.2.3 Community
221
12.2.4 Application Based on the Software Engineering Ontology
223
12.3 Formal Specifications of Software Engineering Domain Conceptualisation
234
12.3.1 SE Ontology Concepts
234
12.3.2 SE Ontology Relations and Constraints
235
12.3.3 SE Ontology Instances
240
12.4 SE Ontology Validation
243
12.4.1 Validation of Communications
243
12.4.2 Validation of Knowledge Sharing
246
12.4.3 Validation of Knowledge Management
250
12.5 Classification of Agents
256
12.6 Need of the Software Engineering Ontology to Support Agent's Intelligence
257
12.7 Agent's Collaborations
257
12.8 Construction of Individual Agents
261
12.8.1 User Agents
261
12.8.2 Safeguard Agents
262
12.8.3 Ontology Agents
262
12.8.4 Decision Making Agents
263
12.9 Practical Uses
264
12.10 Conclusion
269
References
269
13 Potential Applications of Ontology-Based Multi-Agent Systems 271
13.1 Overview
271
13.2 Collaborative Environments
271
13.3 Information Access and Retrieval
272
13.4 Data Mining
272
13.5 Open Issues
273
Dr Maja Hadzic received PhD from Curtin University of Technology in 2006. Her PhD thesis is entitled: "Ontology-based multi-agent systems for human disease studies". She is currently Research Fellow at the Digital Ecosystems and Business Intelligence Institute of the Curtin University of Technology. She has a multi-disciplinary background and her current research interests include ontologies, multi-agent systems, data mining, digital ecosystems and mental health.









Professor Elizabeth Chang is Professor of Information Systems and Software Engineering at the Curtin University of Technology. She is the Director of the Digital Ecosystems and Business Intelligence Institute. Professor Chang has an extensive background, knowledge and skills in academia, commerce and industry.









Dr Pornpit Wongthongtham received PhD from Curtin University of Technology in 2006 and has become an expert in the field of ontology-based multi-agent systems. Over the last 3 years she co-authored numerous papers on ontology-based multi-agent systems for multi-site software engineering. She is currently working as Research Fellow at the Digital Ecosystems and Business Intelligence Institute of the Curtin University of Technology.









Professor Tharam Dillon is an expert in the field of software engineering, data mining XML based systems, ontologies, trust, security and component-oriented access control. Professor Dillon has published five authored books and four co-edited books. He has also published over 600 scientific papers in refereed journals and conferences. Over the last fifteen years, he has been cited, one or more times, in over 1200 publications. This indicates the high impact of his research work.