Ontologies for Agents: Theory and Experiences

von: Valentina Tamma, Stephen Cranefield, Timothy W. Finin, Steven Willmott

Birkhäuser Basel, 2006

ISBN: 9783764373610

Sprache: Englisch

352 Seiten, Download: 4186 KB

Format: PDF, auch als Online-Lesen

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Ontologies for Agents: Theory and Experiences

	Contents	6
	Foreword	8
	Ontologies for Interaction Protocols	12
	1. Introduction	12
	2. Example: the FIPA Request Interaction Protocol	14
	3. A Coloured Petri Net approach	16
	4. Modelling Internal Agent Operations	20
	4.1. The Static Approach	21
	4.2. The Dynamic Approach	22
	5. AUML Revisited	23
	6. Conclusion	24
	References	25
	On the Impact of Ontological Commitment	29
	1. Introduction	29
	2. Ontological Commitment	31
	2.1. Conflict: Definer, Application, User	31
	2.2. Conflict: Commitment, Evolution	32
	3. Setting: The EDEN Application	33
	4. Impedance Mismatch and its Consequences	34
	5. Ontology Development Issues	39
	5.1. Design and Representation	39
	5.2. Evolution and Versioning	40
	6. Compositional Ontologies	41
	6.1. Subset	42
	6.2. Compose	44
	6.3. Extend	45
	6.4. An Example	46
	7. Implementation Issues	46
	7.1. Internal Representation	47
	7.2. External Representation	48
	8. Conclusions	49
	References	51
	Agent to Agent Talk: Nobody There? Supporting Agents Linguistic Communication	53
	1. Introduction	54
	2. How many ways can we misunderstand?	56
	2.1. It’s Greek to me: the role of language in agent communication	57
	2.2. Mapping, merging and messing up with knowledge: different approaches to reconcile heterogeneity	58
	2.3. One for all and all for one: on the ambiguity among terms and concepts	60
	2.4. A rose is a rose is a rose: symbols and meaning	61
	3. Agent ontologies	62
	3.1. Ontological similarity evaluation	63
	3.1.1. Conceptual similarity among planes O(S) and O(H).	64
	3.1.2. Lexical similarity among planes V (S) and V (H).	66
	3.1.3. Lexical expressivity.	67
	3.1.4. Lexical-Semantic coherence.	68
	4. Toward a linguistic agent society: a language-aware architecture	69
	4.1. Agent Taxonomy	70
	4.1.1. The Resource agents.	71
	4.1.2. The Mediator Agent.	72
	4.1.3. The Translator Agent.	73
	4.1.4. The Coordinator agent.	73
	4.2. Adaptive agent communication	75
	4.2.1. Processing agent connections.	75
	4.2.2. Processing agent requests.	76
	5. Conclusions	78
	References	80
	Ontology Translation by Ontology Merging and Automated Reasoning	83
	1. Introduction	83
	2. Our Approach	85
	2.1. Uniform Internal Representation	85
	2.2. Ontology Merging and Bridging Axioms	86
	2.3. Automated Reasoning	91
	3. Application: OntoMerge	93
	4. Recent Work	96
	4.1. Backward Chaining	96
	4.2. Semiautomatic Tools for Ontology Merging	96
	5. Related work	99
	6. Conclusions	101
	References	102
	Collaborative Understanding of Distributed Ontologies in a Multiagent Framework: Experiments on Operational Issues	105
	1. Introduction	105
	2. Framework	107
	2.1. Ontological Components	107
	2.2. Operational Components	108
	2.2.1. Query Processing.	109
	2.2.2. Action Planning.	110
	2.2.3. Query Composition.	110
	2.3. Agent Communication Language	110
	3. Methodology and Design	111
	4. Implementation	113
	5. Discussion of Results	114
	5.1. Experimental Setup	114
	5.2. Parameters Collected	116
	5.3. Results	118
	5.3.1. Level-0 Analysis.	118
	5.3.2. Other Level Analysis.	125
	6. Conclusions	128
	References	129
	Reconciling Implicit and Evolving Ontologies for Semantic Interoperability	131
	1. Introduction	131
	2. Current projects toward a semantic web	132
	3. Reconciling implicit ontologies	134
	4. Practical reconciliation	135
	4.1. CASA	135
	4.2. AReXS	138
	4.3. Modifications to the AReXS algorithm	147
	5. Multi-agent systems: applied semantic interoperability	148
	6. Conclusions and Future Directions	150
	Acknowledgements	151
	References	152
	Query Processing in Ontology-Based Peer-to-Peer Systems	155
	1. Introduction	155
	1.1. Semantic Web and Peer-to-Peer	155
	1.2. The Need for New Approaches	157
	1.2.1. Dropping the global schema.	157
	1.2.2. Good enough answers.	158
	2. Ontology-Based Peer-to-peer Systems	159
	2.1. Ontological Knowledge	160
	2.2. Inter-Ontology Mappings	161
	2.3. Semantics and Logical Consequence	162
	2.4. Ontology-Based Queries	163
	3. Query Processing	164
	3.1. Approximating Class Descriptions	164
	3.2. Queries as Classes	166
	3.3. Quality of Approximation	167
	4. Query Relaxation	168
	4.1. Variable Elimination	169
	4.2. Guided Elimination	171
	5. Examples from a case study	171
	5.1. Concept approximations	172
	5.2. Query relaxation	173
	6. Conclusions	174
	References	176
	Message Content Ontologies	178
	1. Introduction	178
	2. Message Content Ontology Framework	179
	2.1. Agent Communication Meta Ontology	181
	2.2. Reference Model	181
	2.2.1. Conversation Domain Ontology.	182
	2.2.2. Performative Ontology.	183
	2.2.3. Protocol Ontology.	185
	2.2.4. Agent Role Ontology.	187
	2.3. Message Content Ontology	187
	2.4. Message Content Ontology Creation	189
	2.4.1. Identification of Conversation Specific Concepts.	189
	2.4.2. Speci.cation of Conversation Specific Concepts.	190
	2.5. Message Content Ontology Application	192
	3. Operationalization of Ontology-based Communication	194
	3.1. Minimal Agent communication ontology	195
	3.2. Defining Message Content Ontologies	197
	3.3. Mapping from Ontology Design to Java Beans	199
	3.4. Message Content Ontology Application	199
	4. Legal Advisor	200
	4.1. Architecture	200
	4.1.1. Law Expert Agent.	200
	4.1.2. Law Services Broker.	202
	4.1.3. Personal Law Assistant.	202
	4.2. Message Content Ontology Design	202
	4.3. Simple Scenario	203
	4.4. Evaluation	204
	5. Discussion	205
	References	207
	Incorporating Complex Mathematical Relations in Web-Portable Domain Ontologies	210
	1. Introduction	210
	2. The EHEP Experimental Analysis	212
	2.1. Event Selection Variables in the EHEP Domain Ontology	212
	2.2. Constant and Function EHEP Event Selection Variables	214
	3. The Principles of Our Approach	216
	4. Explicating the Mathematical Relations In the EHEP Domain Ontology	218
	4.1. Representing Quantity	218
	4.2. Representing Units of Measurement	222
	4.3. Quantity and Data Type	224
	4.3.1. Basic Data Type.	225
	4.3.2. Composite Data Type.	226
	4.4. Structuring Mathematical Concept as Compound Quantity	229
	4.4.1. Result of Compound Quantity.	231
	4.4.2. Intension of Compound Quantity.	231
	4.4.3. Parameter of Compound Quantity.	231
	5. Encoding the Arithmetic-Logic Expression of Compound Quantities	236
	6. Future Work	236
	7. Conclusion	238
	References	238
	Acknowledgment	240
	The SOUPA Ontology for Pervasive Computing	241
	1. Introduction	241
	2. Problems in the Existing Pervasive Computing Systems	242
	3. The SOUPA Ontology	243
	3.1. The Web Ontology Language OWL	245
	3.2. Related Ontologies	245
	3.3. SOUPA Core	246
	3.4. SOUPA Extension	254
	4. The Context Broker Architecture	255
	5. CoBrA Applications	257
	5.1. The EasyMeeting System	257
	5.2. CoBrA Demo Toolkit	259
	6. Future Work	262
	7. Conclusions	263
	References	263
	A UML Ontology and Derived Content Language for a Travel Booking Scenario	267
	1. Introduction	267
	2. Overview of our approach	268
	3. A travel booking ontology in UML	271
	4. The ontology-specific content language	274
	5. Using the generated content language	275
	6. Comparison with JADE	278
	6.1. Ontologies vs. content languages	279
	6.2. Concept names vs. function symbols	280
	6.3. Terms vs. IREs	281
	6.4. Strongly vs. weakly typed descriptor classes	281
	6.5. Content language codecs	281
	6.6. Generating action description classes	281
	7. Conclusion	281
	References	282
	Some Experiences with the Use of Ontologies in Deliberative Agents	285
	1. Introduction	285
	2. Outline Problem	286
	2.1. Issues from the challenge problem	286
	3. Overview of the Nuin Platform	288
	3.1. Nuinscript scripting knowledge representation language	289
	3.2. Nuinscript plan language	290
	4. Solution Examples using Nuin	291
	4.1. Preamble: use of ontologies	291
	4.2. Initial client to agent communication	293
	4.2.1. Representation of user goals.	295
	4.3. Interactions with suppliers	297
	4.4. Reconciling vocabularies	299
	4.5. Critiquing and ranking solutions	300
	5. Evaluation and conclusions	303
	References	304
	Location-Mediated Agent Coordination in Ubiquitous Computing	307
	1. Introduction	307
	2. Coordination Gaps in Ubiquitous Computing	308
	2.1. Intention Gaps between Services, Devices and Humans	308
	2.2. Representation Gaps between Services, Devices and Humans	309
	3. Location-Mediated Agent Coordination	309
	3.1. Bridging Intention Gaps between Services, Devices and Humans	311
	3.2. Bridging Representation Gaps between Services, Devices and Humans	311
	4. Implementation	323
	5. Related Work	326
	6. Future Work	326
	7. Conclusion	327
	References	327
	Acknowledgment	329
	An Ontology for Agent-Based Monitoring of Fulfillment Processes	330
	1. Problem	330
	2. Supply Chain Monitoring	332
	2.1. Supply Chain Model	332
	2.2. Agent-Based Concept	333
	3. Ontology	336
	3.1. Methodological Approach	336
	3.2. Tracking Data	337
	3.3. Concepts	338
	3.4. Supply Chain Scenario	342
	4. Implementation	343
	5. Ontology-Based Agent Communication	344
	6. Prototype Systems	347
	7. Conclusion	350
	References	350
	Acknowledgment	352

Ontologies for Agents: Theory and Experiences

von: Valentina Tamma, Stephen Cranefield, Timothy W. Finin, Steven Willmott

Ontologies for Agents: Theory and Experiences

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