Semantic Agent Systems


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