Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
About retrieval models and logic
The Computer Journal - Special issue on information retrieval
Information filtering and information retrieval: two sides of the same coin?
Communications of the ACM - Special issue on information filtering
General patterns in nonmonotonic reasoning
Handbook of logic in artificial intelligence and logic programming (vol. 3)
Handbook of logic in artificial intelligence and logic programming (vol. 3)
Investigating aboutness axioms using information fields
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
A personal news agent that talks, learns and explains
Proceedings of the third annual conference on Autonomous Agents
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Amalthaea: An Evolving Multi-Agent Information Filtering and Discovery System for the WWW
Autonomous Agents and Multi-Agent Systems
IEEE Transactions on Knowledge and Data Engineering
A Study of Belief Revision in the Context of Adaptive Information Filtering
ICSC '99 Proceedings of the 5th International Computer Science Conference on Internet Applications
Using Default Logic for Lexical Knowledge
ECSQARU/FAPR '97 Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning
Letizia: an agent that assists web browsing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Selected new training documents to update user profile
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Structure based semantic measurement for information filtering agents
AOW '07 Proceedings of the Third Australasian Workshop on Advances in Ontologies - Volume 85
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The general goal of information retrieval (IR) and information filtering (IF) is to dispatch relevant information objects to a user with respect to their specific information need. Such a process can be approximated by matching the representation K of a user's information needs with the description d of each incoming information object. Since users' information needs will change over time, the matching process demonstrates nonmonotonicity in general. Moreover, as both K and d are only the partial descriptions of the underlying entities, uncertainty and inconsistency may arise during information matching. With a logic-based approach, the matching process can be characterised by K d, where is a nonmonotonic inference relation. This paper examines how the non-trivial possibilistic deduction, a well-behaved nonmonotonic inference relation, can be applied to develop adaptive information filtering agents for alleviating information overload on the Web.