IEEE Intelligent Systems
Different Aspects of Social Network Analysis
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
A survey of trust in computer science and the Semantic Web
Web Semantics: Science, Services and Agents on the World Wide Web
Trust based ontology integration for the community services sector
AOW '06 Proceedings of the second Australasian workshop on Advances in ontologies - Volume 72
TREPPS: A Trust-based Recommender System for Peer Production Services
Expert Systems with Applications: An International Journal
On boosting semantic web data access
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 4
Preferential reasoning on a web of trust
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
A scalable probabilistic approach to trust evaluation
iTrust'06 Proceedings of the 4th international conference on Trust Management
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The Semantic Web enables intelligent agents to "outsource" knowledge, extending and enhancing their limited knowledge bases. An open question is how agents can efficiently and effectively access the vast knowledge on the inherently open and dynamic Semantic Web. Teh problem is not that of finding a source for desired information, but deciding which among many possibly inconsistent sources is most reliable. We propose an approach to agent knowledge outsourcing inspired by the use trust in human society. Trust is a type of social knowledge and encodes evaluations about which agents can be taken as a reliable sources of information or services. We focus on two important practical issues: learning trust and justifying trust. An agent can learn trust relationships by reasoning about its direct interactions with other agents and about public or private reputation information, i.e., the aggregate trust evaluations of other agents. We use the term trust justification to describe the process in which an agent integrates the beliefs of other agents, trust information, and its own beliefs to update its trust model. We describe the results of simulation experiments of the use and evolution of trust in multi-agent systems. Our experiments demonstrate that the use of explicit trust knowledge can significantly improve knowledge outsourcing performance. We also describe a collaborative trust justification technique that focuses on reducing search complexity, handling inconsistent knowledge, and avoiding error propagation.