Artificial Intelligence
A learning interface agent for scheduling meetings
IUI '93 Proceedings of the 1st international conference on Intelligent user interfaces
Agents that reduce work and information overload
Communications of the ACM
COACH: a teaching agent that learns
Communications of the ACM
The Rufus System: Information Organization for Semi-Structured Data
VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
Type Classification of Semi-Structured Documents
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
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The huge volume of distributed information that is nowadays available in electronic multimedia documents forces a lot of people to consume a significant percentage of their time looking for documents that contain information useful to them. The filtering of electronic documents seems hard to automate, partly because of document heterogeneity, but mainly because it is difficult to train computers to have an understanding of the contents of these documents and make decisions based on user-subjective criteria. In this paper, we suggest a model for the automation of content-based electronic document filtering, supporting multimedia documents in a wide variety of forms. The model is based on multi-agent technology and utilizes an adaptive knowledge base organized as a set of logical rules. Implementations of the model using the client-server architecture should be able to efficiently access documents distributed over an intranet or the Internet.