Tamma / Cranefield / Finin Ontologies for Agents: Theory and Experiences

2005
ISBN: 978-3-7643-7361-0
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 352 Seiten

Reihe: Whitestein Series in Software Agent Technologies and Autonomic Computing

The volume aims at providing a comprehensive review of the diverse efforts covering the gap existing between the two main perspectives on the topic of ontologies for multi-agent systems, namely: How ontologies should be modelled and represented in order to be effectively used in agent systems, and on the other hand, what kind of capabilities should be exhibited by an agent in order to make use of ontological knowledge and to perform efficient reasoning with it. The volume collects the most significant papers of the AAMAS 2002 and AAMAS 2003 workshop on ontologies for agent systems, and the EKAW 2002 workshop on ontologies for multi-agent systems.

Autoren/Hrsg.

Tamma, Valentina

Cranefield, Stephen

Finin, Timothy W.

Willmott, Steven

Inhaltsverzeichnis

1;Contents;6
2;Foreword;8
3;Ontologies for Interaction Protocols;12
3.1;1. Introduction;12
3.2;2. Example: the FIPA Request Interaction Protocol;14
3.3;3. A Coloured Petri Net approach;16
3.4;4. Modelling Internal Agent Operations;20
3.4.1;4.1. The Static Approach;21
3.4.2;4.2. The Dynamic Approach;22
3.5;5. AUML Revisited;23
3.6;6. Conclusion;24
3.7;References;25
4;On the Impact of Ontological Commitment;29
4.1;1. Introduction;29
4.2;2. Ontological Commitment;31
4.2.1;2.1. Conflict: Definer, Application, User;31
4.2.2;2.2. Conflict: Commitment, Evolution;32
4.3;3. Setting: The EDEN Application;33
4.4;4. Impedance Mismatch and its Consequences;34
4.5;5. Ontology Development Issues;39
4.5.1;5.1. Design and Representation;39
4.5.2;5.2. Evolution and Versioning;40
4.6;6. Compositional Ontologies;41
4.6.1;6.1. Subset;42
4.6.2;6.2. Compose;44
4.6.3;6.3. Extend;45
4.6.4;6.4. An Example;46
4.7;7. Implementation Issues;46
4.7.1;7.1. Internal Representation;47
4.7.2;7.2. External Representation;48
4.8;8. Conclusions;49
4.9;References;51
5;Agent to Agent Talk: Nobody There? Supporting Agents Linguistic Communication;53
5.1;1. Introduction;54
5.2;2. How many ways can we misunderstand?;56
5.2.1;2.1. It’s Greek to me: the role of language in agent communication;57
5.2.2;2.2. Mapping, merging and messing up with knowledge: different approaches to reconcile heterogeneity;58
5.2.3;2.3. One for all and all for one: on the ambiguity among terms and concepts;60
5.2.4;2.4. A rose is a rose is a rose: symbols and meaning;61
5.3;3. Agent ontologies;62
5.3.1;3.1. Ontological similarity evaluation;63
5.3.1.1;3.1.1. Conceptual similarity among planes O(S) and O(H).;64
5.3.1.2;3.1.2. Lexical similarity among planes V (S) and V (H).;66
5.3.1.3;3.1.3. Lexical expressivity.;67
5.3.1.4;3.1.4. Lexical-Semantic coherence.;68
5.4;4. Toward a linguistic agent society: a language-aware architecture;69
5.4.1;4.1. Agent Taxonomy;70
5.4.1.1;4.1.1. The Resource agents.;71
5.4.1.2;4.1.2. The Mediator Agent.;72
5.4.1.3;4.1.3. The Translator Agent.;73
5.4.1.4;4.1.4. The Coordinator agent.;73
5.4.2;4.2. Adaptive agent communication;75
5.4.2.1;4.2.1. Processing agent connections.;75
5.4.2.2;4.2.2. Processing agent requests.;76
5.5;5. Conclusions;78
5.6;References;80
6;Ontology Translation by Ontology Merging and Automated Reasoning;83
6.1;1. Introduction;83
6.2;2. Our Approach;85
6.2.1;2.1. Uniform Internal Representation;85
6.2.2;2.2. Ontology Merging and Bridging Axioms;86
6.2.3;2.3. Automated Reasoning;91
6.3;3. Application: OntoMerge;93
6.4;4. Recent Work;96
6.4.1;4.1. Backward Chaining;96
6.4.2;4.2. Semiautomatic Tools for Ontology Merging;96
6.5;5. Related work;99
6.6;6. Conclusions;101
6.7;References;102
7;Collaborative Understanding of Distributed Ontologies in a Multiagent Framework: Experiments on Operational Issues;105
7.1;1. Introduction;105
7.2;2. Framework;107
7.2.1;2.1. Ontological Components;107
7.2.2;2.2. Operational Components;108
7.2.2.1;2.2.1. Query Processing.;109
7.2.2.2;2.2.2. Action Planning.;110
7.2.2.3;2.2.3. Query Composition.;110
7.2.3;2.3. Agent Communication Language;110
7.3;3. Methodology and Design;111
7.4;4. Implementation;113
7.5;5. Discussion of Results;114
7.5.1;5.1. Experimental Setup;114
7.5.2;5.2. Parameters Collected;116
7.5.3;5.3. Results;118
7.5.3.1;5.3.1. Level-0 Analysis.;118
7.5.3.2;5.3.2. Other Level Analysis. ;125
7.6;6. Conclusions;128
7.7;References;129
8;Reconciling Implicit and Evolving Ontologies for Semantic Interoperability;131
8.1;1. Introduction;131
8.2;2. Current projects toward a semantic web;132
8.3;3. Reconciling implicit ontologies;134
8.4;4. Practical reconciliation;135
8.4.1;4.1. CASA;135
8.4.2;4.2. AReXS;138
8.4.3;4.3. Modifications to the AReXS algorithm;147
8.5;5. Multi-agent systems: applied semantic interoperability;148
8.6;6. Conclusions and Future Directions;150
8.7;Acknowledgements;151
8.8;References;152
9;Query Processing in Ontology-Based Peer-to-Peer Systems;155
9.1;1. Introduction;155
9.1.1;1.1. Semantic Web and Peer-to-Peer;155
9.1.2;1.2. The Need for New Approaches;157
9.1.2.1;1.2.1. Dropping the global schema.;157
9.1.2.2;1.2.2. Good enough answers.;158
9.2;2. Ontology-Based Peer-to-peer Systems;159
9.2.1;2.1. Ontological Knowledge;160
9.2.2;2.2. Inter-Ontology Mappings;161
9.2.3;2.3. Semantics and Logical Consequence;162
9.2.4;2.4. Ontology-Based Queries;163
9.3;3. Query Processing;164
9.3.1;3.1. Approximating Class Descriptions;164
9.3.2;3.2. Queries as Classes;166
9.3.3;3.3. Quality of Approximation;167
9.4;4. Query Relaxation;168
9.4.1;4.1. Variable Elimination;169
9.4.2;4.2. Guided Elimination;171
9.5;5. Examples from a case study;171
9.5.1;5.1. Concept approximations;172
9.5.2;5.2. Query relaxation;173
9.6;6. Conclusions;174
9.7;References;176
10;Message Content Ontologies;178
10.1;1. Introduction;178
10.2;2. Message Content Ontology Framework;179
10.2.1;2.1. Agent Communication Meta Ontology;181
10.2.2;2.2. Reference Model;181
10.2.2.1;2.2.1. Conversation Domain Ontology.;182
10.2.2.2;2.2.2. Performative Ontology.;183
10.2.2.3;2.2.3. Protocol Ontology.;185
10.2.2.4;2.2.4. Agent Role Ontology.;187
10.2.3;2.3. Message Content Ontology;187
10.2.4;2.4. Message Content Ontology Creation;189
10.2.4.1;2.4.1. Identification of Conversation Specific Concepts.;189
10.2.4.2;2.4.2. Speci.cation of Conversation Specific Concepts.;190
10.2.5;2.5. Message Content Ontology Application;192
10.3;3. Operationalization of Ontology-based Communication;194
10.3.1;3.1. Minimal Agent communication ontology;195
10.3.2;3.2. Defining Message Content Ontologies;197
10.3.3;3.3. Mapping from Ontology Design to Java Beans;199
10.3.4;3.4. Message Content Ontology Application;199
10.4;4. Legal Advisor;200
10.4.1;4.1. Architecture;200
10.4.1.1;4.1.1. Law Expert Agent.;200
10.4.1.2;4.1.2. Law Services Broker.;202
10.4.1.3;4.1.3. Personal Law Assistant.;202
10.4.2;4.2. Message Content Ontology Design;202
10.4.3;4.3. Simple Scenario;203
10.4.4;4.4. Evaluation;204
10.5;5. Discussion;205
10.6;References;207
11;Incorporating Complex Mathematical Relations in Web-Portable Domain Ontologies;210
11.1;1. Introduction;210
11.2;2. The EHEP Experimental Analysis;212
11.2.1;2.1. Event Selection Variables in the EHEP Domain Ontology;212
11.2.2;2.2. Constant and Function EHEP Event Selection Variables;214
11.3;3. The Principles of Our Approach;216
11.4;4. Explicating the Mathematical Relations In the EHEP Domain Ontology;218
11.4.1;4.1. Representing Quantity;218
11.4.2;4.2. Representing Units of Measurement;222
11.4.3;4.3. Quantity and Data Type;224
11.4.3.1;4.3.1. Basic Data Type.;225
11.4.3.2;4.3.2. Composite Data Type.;226
11.4.4;4.4. Structuring Mathematical Concept as Compound Quantity;229
11.4.4.1;4.4.1. Result of Compound Quantity.;231
11.4.4.2;4.4.2. Intension of Compound Quantity.;231
11.4.4.3;4.4.3. Parameter of Compound Quantity.;231
11.5;5. Encoding the Arithmetic-Logic Expression of Compound Quantities;236
11.6;6. Future Work;236
11.7;7. Conclusion;238
11.8;References;238
11.9;Acknowledgment;240
12;The SOUPA Ontology for Pervasive Computing;241
12.1;1. Introduction;241
12.2;2. Problems in the Existing Pervasive Computing Systems;242
12.3;3. The SOUPA Ontology;243
12.3.1;3.1. The Web Ontology Language OWL;245
12.3.2;3.2. Related Ontologies;245
12.3.3;3.3. SOUPA Core;246
12.3.4;3.4. SOUPA Extension;254
12.4;4. The Context Broker Architecture;255
12.5;5. CoBrA Applications;257
12.5.1;5.1. The EasyMeeting System;257
12.5.2;5.2. CoBrA Demo Toolkit;259
12.6;6. Future Work;262
12.7;7. Conclusions;263
12.8;References;263
13;A UML Ontology and Derived Content Language for a Travel Booking Scenario;267
13.1;1. Introduction;267
13.2;2. Overview of our approach;268
13.3;3. A travel booking ontology in UML;271
13.4;4. The ontology-specific content language;274
13.5;5. Using the generated content language;275
13.6;6. Comparison with JADE;278
13.6.1;6.1. Ontologies vs. content languages;279
13.6.2;6.2. Concept names vs. function symbols;280
13.6.3;6.3. Terms vs. IREs;281
13.6.4;6.4. Strongly vs. weakly typed descriptor classes;281
13.6.5;6.5. Content language codecs;281
13.6.6;6.6. Generating action description classes;281
13.7;7. Conclusion;281
13.8;References;282
14;Some Experiences with the Use of Ontologies in Deliberative Agents;285
14.1;1. Introduction;285
14.2;2. Outline Problem;286
14.2.1;2.1. Issues from the challenge problem;286
14.3;3. Overview of the Nuin Platform;288
14.3.1;3.1. Nuinscript scripting knowledge representation language;289
14.3.2;3.2. Nuinscript plan language;290
14.4;4. Solution Examples using Nuin;291
14.4.1;4.1. Preamble: use of ontologies;291
14.4.2;4.2. Initial client to agent communication;293
14.4.2.1;4.2.1. Representation of user goals.;295
14.4.3;4.3. Interactions with suppliers;297
14.4.4;4.4. Reconciling vocabularies;299
14.4.5;4.5. Critiquing and ranking solutions;300
14.5;5. Evaluation and conclusions;303
14.6;References;304
15;Location-Mediated Agent Coordination in Ubiquitous Computing;307
15.1;1. Introduction;307
15.2;2. Coordination Gaps in Ubiquitous Computing;308
15.2.1;2.1. Intention Gaps between Services, Devices and Humans;308
15.2.2;2.2. Representation Gaps between Services, Devices and Humans;309
15.3;3. Location-Mediated Agent Coordination;309
15.3.1;3.1. Bridging Intention Gaps between Services, Devices and Humans;311
15.3.2;3.2. Bridging Representation Gaps between Services, Devices and Humans;311
15.4;4. Implementation;323
15.5;5. Related Work;326
15.6;6. Future Work;326
15.7;7. Conclusion;327
15.8;References;327
15.9;Acknowledgment;329
16;An Ontology for Agent-Based Monitoring of Fulfillment Processes;330
16.1;1. Problem;330
16.2;2. Supply Chain Monitoring;332
16.2.1;2.1. Supply Chain Model;332
16.2.2;2.2. Agent-Based Concept;333
16.3;3. Ontology;336
16.3.1;3.1. Methodological Approach;336
16.3.2;3.2. Tracking Data;337
16.3.3;3.3. Concepts;338
16.3.4;3.4. Supply Chain Scenario;342
16.4;4. Implementation;343
16.5;5. Ontology-Based Agent Communication;344
16.6;6. Prototype Systems;347
16.7;7. Conclusion;350
16.8;References;350
16.9;Acknowledgment;352

Leseproben

Reconciling Implicit and Evolving Ontologies for Semantic Interoperability (p. 121-122)

Kendall Lister, Maia Hristozova and Leon Sterling

Abstract. This paper addresses current approaches to the goal of semantic interoperability on the web and presents new research directions. We critically discuss the existing approaches, including RDF, SHOE, PROMPT and Chimaera, and identify the most e.ective elements of each. In our opinion, the ability of these primarily closed solutions to succeed on a global web scale is limited. In general, a unilateral solution to the problem on a global level seems unlikely in the foreseeable future. We review and contrast our own research experiments AReXS and CASA and suggest that as yet unaddressed issues should be considered, such as reconciling implicit ontologies and evolving ontologies and task-oriented analysis. We also consider the role of semantic interoperation in multi-agent systems and describe strategies for achieving this via the ROADMAP methodology, with emphasis on building and assuring knowledge models.

Keywords. Ontology translation/mapping, Ontology maintenance/evolution, Data standardisation.

1. Introduction

The much talked about goal of building a new Internet that is comprehensible to machines as well as humans is generally considered to involve enhancing content and information sources with semantic markings and explicit ontologies. A number of approaches to this goal have been proposed, and these generally involve a new representation for semantically enriched data. Something that seems to be often overlooked, however, is that a single solution is unlikely to be usefully applicable to the entire world wide web. It is obvious that business needs are generally quite di.erent to the needs of individuals, and that even within the business community di.erent areas will require solutions of varying sophistication, accuracy and scale. The widespread success of the world wide web and its underlying technologies, HTML and HTTP, has been due in no small part to their simplicity and ease of adoption. By providing a simple architecture that anyone could learn and use with minimal overhead, content .ourished on the web. Other information technologies that arguably provided more e.ective methods for locating and retrieving data failed to take o. in the same exponential way that the web did.

Where the web infrastructure itself doesn’t even contain the most rudimentary searching and resource location features, Gopher, WAIS and a large number of proprietary online databases that predated the world wide web all provided automated indexing, searching, hypertextuality and other information management capabilities. But despite their apparent advantages, all of these technologies were overtaken by the web. In fact, in many cases proprietary databases and indexes have had their interfaces replaced with web-based solutions, to the point that the actual technology is largely hidden. It is more than a coincidence that where the world wide web succeeded and grew to become a de facto standard, the more complex alternatives faltered and missed out on popular adoption.

Similarly, we consider that the next generation of semantically-capable global information infrastructure will necessarily be relatively simple in order to achieve the same scale of acceptance. That is not to say that sophisticated technologies have no place - on the contrary, they will be vital for the areas of industry that require them, and their advances will no doubt drive other research e.orts even further. Also, the intelligent agents that roam this infrastructure will themselves be very sophisticated. However, there remains a fundamental role for simple, .exible and adaptive technologies that do not demand strict adherence to formal standards and protocols and the development and publishing costs that follow. By leaving the majority of the intelligence for semantic comprehension in the interpreting applications rather than the medium itself, we will develop technologies that can operate in any information environment, not just those that are sophisticated and semantically enhanced. There is no suggestion that semantically rich environments are not useful and desirable, but it is not practical to expect the entirety, or even the majority, of the information landscape of the future to be uniformly structured, as current research seems to imagine.

2. Current projects toward a semantic web

Discussions of the problems of semantic operability on the web have a tendency to become discussions of the problem of managing and integrating ontologies. The reasons for this are not obscure: ontologies are widely regarded as a critical element of the next generation of data integration solutions, and the world wide web is a heterogeneous environment in which foreign data (and therefore ontologies) are regularly juxtaposed. What is less clear is how such data can be combined. A number of new technologies have been proposed that extend or replace existing web technologies, prominent among these are RDF, SHOE, PROMPT and Chimaera.