Context-Centric Needs Anticipation Using Information Needs Graphs

Authors:
Xiaocong Fan;Rui Wang;Shuang Sun;John Yen;Richard A. Volz
Affiliations:
School of Information Sciences and Technology, The Pennsylvania State University 16802;School of Information Sciences and Technology, The Pennsylvania State University 16802;School of Information Sciences and Technology, The Pennsylvania State University 16802;School of Information Sciences and Technology, The Pennsylvania State University 16802;Department of Computer Science, Texas A&M University, College Station 77843
Venue:
Applied Intelligence
Year:
2006

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Abstract

Effective agent teamwork requires information exchange to be conducted in a proactive, selective, and intelligent way. In the field of distributed artificial intelligence, there has been increasing number of research focusing on need-driven proactive communication, both theoretically and practically. Among these works, CAST has realized a team-oriented agent architecture where agents, based on a computational shared mental model, are able to anticipate teammates' information needs and proactively deliver relevant information to the needers in a timely manner. However, the first implementation of CAST takes little consideration of the dynamics of the anticipated information needs, which can change in various ways as the context develops. In this paper we describe a novel mechanism for organizing and managing the "context" of information needs. This allows agents to dynamically activate and deactivate information needs progressively. It has been shown that the two-level context-centric approach can enhance team performance considerably.