Semantic Agent Systems: Foundations and Applications (2011) .. Atilla Elçi @atillaelci etc eds
Contents
Part I: Introduction to Agents and Semantics
1 Rule Responder Agents Framework and Instantiations … 3
Harold Boley, Adrian Paschke
1 Introduction … 3
2 The Rule Responder Framework … 5
2.1 Mule Enterprise Service Bus … 5
2.2 Selected Platform-Specific Rule Engines for Rule Responder Agents … 7
3 Rule Responder Agents … 12
3.1 Organizational Agent … 12
3.2 Personal Agents … 13
3.3 External Agents … 14
3.4 Responsibility Assignment Matrix … 14
4 Translation between Rule Responder Agents … 16
5 Rule Responder Instantiations … 18
5.1 SymposiumPlanner … 18
5.2 WellnessRules … 18
5.3 PatientSupporter … 19
5.4 Reputation Management System … 21
5.5 Semantic Complex Event Processing Agent Network … 21
6 Conclusion … 22
References … 22
2 Specifying and Monitoring Obligations in Open Multiagent Systems Using Semantic Web Technology … 25
Nicoletta Fornara
1 Introduction … 25
2 Other Approaches … 28
3 OWL and SWRL … 29
4 An Application Independent Ontology for Modelling and Monitoring Agents’ interactions … 30
4.1 Modelling Time, Events, and Fluents … 30
4.2 An Example of a Domain Dependent Ontology … 32
4.3 Representing Events, Actions, and the Elapsing of Time … 32
4.4 Representing Specific Obligations … 34
4.5 Monitoring the State of Obligations … 37
4.6 Possible Type of Obligations … 39
5 A Case Study: Obligations in Vehicle Repair Contracts … 41
6 Conclusions and Future Works … 43
References … 44
3 Programming Semantic Agent for Distributed Knowledge Management … 47
Julien Subercaze, Pierre Maret
1 Introduction and Motivation … 47
2 Building Agents with Semantic Rules … 49
2.1 Architecture Design … 50
2.2 SAM Architecture … 50
2.2.1 Knowledge Base … 51
2.2.2 Engine … 51
2.2.3 Low Level Actions and MAS Framework … 52
2.3 Control Structure … 52
2.4 Execution Stack … 53
2.5 Language Syntax … 55
3 Semantic Agent Model … 56
3.1 Defining New Actions … 58
4 Example … 59
4.1 Execution Phase … 61
5 Implementation … 62
6 Perspectives … 62
7 Conclusion … 63
References … 63
Part II: Engineering Semantic Agent Systems
4 SBVR-Driven Information Governance: A Case Study in the Flemish Public Administration … 69
Pieter De Leenheer, Aldo de Moor, Stijn Christiaens
1 Closed World Syndrome … 70
2 Just-in-Time Information … 70
3 The Gap between Business and Technical Metadata … 71
4 Business Drivers to Bridge the Gap … 72
4.1 Documentation … 72
4.2 Communication … 72
4.3 Reuse … 73
4.4 Impact Analysis … 73
4.5 Disambiguation … 73
4.6 Uniformity … 74
4.7 Compliance … 74
5 Metadata Landscape Dimensions … 74
6 Metadata Landscape SWOT Analysis … 75
7 Business Semantics Management … 76
7.1 Fact-Orientation … 77
7.2 Collaborative Business Semantics Modelling with SBVR … 77
7.3 Business Semantics Structure … 78
7.4 Business Semantics in Practice … 80
8 Business Semantics Glossary … 81
8.1 Enterprise Information Model … 83
9 Full-Cycle BSM: Validation and Feedback … 84
9.1 IT/IS-Driven Validation … 84
9.2 Business-Driven Validation … 85
10 Metadata Architecture and Governance … 85
11 Conclusion … 86
References … 87
5 Argumentation for Reconciling Agent Ontologies … 89
Cássia Trojahn, Jérôme Euzenat, Valentina Tamma, Terry R. Payne
1 Introduction … 89
2 Foundations: Alignment and Argumentation Frameworks … 91
2.1 Ontology Mapping … 91
2.2 Argumentation Frameworks … 94
3 Argumentation Frameworks for Alignment Agreement … 96
3.1 Arguments on Correspondences … 96
3.2 Strength-Based Argumentation Framework (SVAF) … 96
3.3 Voting-Based Argumentation Framework (VVAF) … 97
4 Argumentation over Alignments … 98
4.1 Argumentation over Alignments for Communication in Multi-agent Systems … 98
4.1.1 Meaning-Based Argumentation … 98
4.1.2 The Approach by Trojahn and Colleagues … 101
4.1.3 Reducing the Argumentation Space through Modularization … 102
4.2 Solving Conflicts between Matcher Agents … 104
5 Weakness and Challenges … 105
6 Other Related Work … 107
7 Final Remarks … 108
References … 108
6 Measuring Complexity for MAS Design in the Presence of Ontology Heterogeneity … 113
Maricela Bravo
1 Introduction … 113
1.1 MAS Communication Overview … 113
1.2 Ontologies for Inter-agent Communication … 115
1.3 Problem Formulation … 115
2 MAS Architectural Design … 116
2.1 Architectural Considerations … 116
2.2 Associated Costs … 117
3 Basic Measures … 118
4 Centralized Architecture … 119
4.1 Translation Costs … 120
4.2 Ontology Costs … 121
5 Distributed Architecture … 122
5.1 Distributed Architecture with Translators … 123
5.2 Distributed Architecture with Learning Capabilities … 124
5.3 Coordination or Intermediation Costs … 125
6 Experimental Case … 125
6.1 Cost of a Centralized Architecture … 127
6.2 Cost of a Distributed Architecture … 128
7 Results Discussion … 129
8 Conclusions … 130
References … 131
7 Ontology-Based Matchmaking and Composition of Business Processes … 133
Duygu Çelik, Atilla Elçi
1 Introduction … 133
2 Contributions … 134
3 Theoretical Background … 136
4 System Architecture … 138
5 Semantic-Based Matching for a Composition Plan … 140
6 Semantic Matching Step (SMS) … 141
7 Revised Armstrong’s Axioms (RAAs) … 146
8 Inferencing in SCA: A Case Study … 149
9 Conclusion … 154
References … 155
8 Semantic Architecture for Human Robot Interaction … 159
Sébastien Dourlens, Amar Ramdane-Chérif
1 Background … 159
2 Related Work … 161
3 Multimodal Interaction Architecture Design … 164
4 Semantic Agent Memory … 167
5 Multimodal Interaction Agents … 171
5.1 Fusion Agent … 171
5.2 Management Agent … 173
5.3 Fission Agent … 174
6 Networking … 176
6.1 Protocols … 176
6.2 Event Messages … 176
6.3 Semantic Agencies … 177
7 Development Platform … 178
8 Application to an Assistant Robot … 180
8.1 Robot Composition … 180
8.2 Robot at home … 180
8.3 Robot in the City … 183
9 Conclusion and Future work … 184
References … 184
Part III: Applications of Semantic Agent Systems
9 A Semantic Agent Framework for Cyber-Physical Systems … 189
Jing Lin, Sahra Sedigh, Ann Miller
1 Introduction … 189
2 Background Work … 192
3 Agent-Based Modeling Technology … 193
3.1 Definition of the Agents … 194
3.2 Construction of an Agent-Based Model … 195
4 Semantic Interpretation Services … 202
4.1 Sensor Information Ontology … 202
4.2 Model for Semantic Services … 204
4.3 Semantic Agent Framework … 205
4.4 Data Type Processing … 207
4.5 Implementation in C++ … 210
5 Conclusions … 211
References … 211
10 A Layered Manufacturing System Architecture Supported with Semantic Agent Capabilities … 215
Munir Merdan, Mathieu Vallée, Thomas Moser, Stefan Biffl
1 Introduction … 215
2 State of the Art … 216
2.1 Centralized Manufacturing System Control … 216
2.2 Multi-Agent Systems as Foundation for Decentralized Control … 217
2.3 Agent Systems Facilitated by Semantic Technologies … 218
3 Research Issues … 218
4 A Layered Manufacturing System Architecture … 219
5 The Management Layer … 221
5.1 Enterprise Resource Planning (ERP) and Virtual Enterprises … 221
5.2 Layers and Agents … 222
5.3 Production Process Cycle … 223
6 The Planning and Scheduling Layers … 225
6.1 Planning … 225
6.2 Application of Agents in Process Planning … 225
6.3 Production Scheduling … 226
6.4 Integration of Process Planning and Scheduling … 226
6.5 Planning and Scheduling in the Assembly Domain … 227
7 The Execution Layer … 230
7.1 Requirements of the Execution Layer … 230
7.2 Semantic Agents for the Execution Layer … 231
The Automation Agent Architecture … 232
Semantic Technologies for Automation Agents … 233
7.3 Lessons Learned – Practical Use of Semantic Agent Technologies …237
8 Conclusion and Further Work … 238
References … 239
11 Semantic Multi-Agent mLearning System … 243
Stanimir Stoyanov, Ivan Ganchev, Máirtín O’Droma, Hussein Zedan, Damien Meere, Veselina Valkanova
1 Introduction … 243
2 Related Works … 244
3 InfoStation-Based Network Architecture … 245
4 Context-Aware Service Provision … 247
5 Layered System Architecture … 249
6 Agent-Oriented Middleware Architecture … 251
7 Using the Ontology Web Language for Services (OWL-S) … 254
8 Context-Aware Management of Service Sessions … 257
9 User-Based Service Contextualisation and Adaptation … 260
10 Sample/mTest Service Provision … 264
11 Implementation Issues … 268
12 Conclusion … 269
References … 270
12 Identifying Novel Topics Based on User Interests … 273
Makoto Nakatsuji
1 Introduction … 273
2 Related Works … 277
3 Collaborative Filtering … 278
4 Modeling User Interests According to the Taxonomy … 279
5 Measuring Similarity of Users … 280
5.1 Approach … 280
5.2 Algorithm … 281
5.3 Example … 281
6 Novel Topic Identification … 281
7 Offline Experiments … 283
7.1 Investigating accuracy … 283
7.1.1 Dataset … 283
7.1.2 Methodology … 283
7.1.3 Compared Methods … 284
7.1.4 Results … 285
7.2 Analyzing Suitable Size of User Group to Identify Novel Topics … 285
7.3 Investigating User Interest Distribution According to the Score of Novelty … 287
8 Online Experimental Results … 287
8.1 Explaining our Online Experiment … 287
8.2 Investigating Continuance of User Access to our Recommendation List … 288
8.3 Evaluating Identification of Novel Topics … 289
8.4 Evaluating Activation of Blog Community … 289
9 Conclusion … 290
References … 291
Part IV: Future Outlook
13 Semantic Agents with Understanding Abilities and Factors Affecting Misunderstanding … 295
Tuncer Ören, Levent Yilmaz
1 Introduction … 295
1.1 Machine Understanding … 296
1.2 Motivation: The Role of Understanding in Decision Support … 296
1.3 Agents, Semantic Agents, and Pivotal Role of Machine Understanding … 297
1.4 Synergies of Agents and Semantic Agents with Simulation and Systems Engineering … 298
2 Machine Understanding Systems and Agents with Understanding Abilities … 299
3 Types of Single Understanding … 300
3.1 Machine Understanding from the Point of View of Product of Understanding … 300
3.2 Machine Understanding from the Point of View of Process to Understand … 301
3.3 Machine Understanding from the Point of View of Meta-Model of Understanding … 301
3.4 Machine Understanding from the Point of View of Characteristics of Understanding System … 302
4 Multi-understanding … 302
4.1 Role of Meta-Models in Multi-understanding … 302
4.2 Role of Perception in Multi-understanding … 303
4.3 Role of Interpretation in Understanding … 303
5 Switchable Understanding … 303
6 Misunderstanding … 303
6.1 Ability/Inability to Understand … 305
6.1.1 Role of Meta-Model in Misunderstanding … 305
6.1.2 Role of Perception in Misunderstanding … 305
6.1.3 Role of Interpretation in Misunderstanding … 305
6.2 Role of Context in Misunderstanding … 305
6.3 Role of Biases in Misunderstanding … 306
6.3.1 Group Bias in Misunderstanding … 306
6.3.2 Cultural Bias in Misunderstanding … 306
6.3.3 Cognitive Bias in Misunderstanding … 306
6.3.4 Emotive Bias in Misunderstanding … 307
6.3.5 Personality Bias in Misunderstanding … 307
6.3.6 Effects of Dysrationalia and Irrationality in Misunderstanding … 307
6.4 Role of Fallacies in Misunderstanding … 307
6.4.1 Deliberate Misunderstanding … 307
6.4.2 Induced Misunderstanding … 308
6.4.3 Mutual Misunderstanding … 308
7 Conclusions and Future Research … 308
References … 308
Appendix A Concepts and Terms Related with Machine Understanding … 310
Appendix B Concepts and Terms Related with Machine Misunderstanding … 312
Author Index … 315