CAST: Collaborative Agents for Simulating Teamwork

John Yen, Jianwen Yin, Thomas R. Ioerger, Michael S. Miller, Dianxiang Xu, Richard A. Volz

Research output: Contribution to journalConference articlepeer-review

136 Scopus citations


Psychological studies on teamwork have shown that an effective team often can anticipate information needs of teammates based on a shared mental model. Existing multi-agent models for teamwork are limited in their ability to support proactive information exchange among teammates. To address this issue, we have developed and implemented a multi-agent architecture called CAST that simulates teamwork and supports proactive information exchange in a dynamic environment. We present a formal model for proactive information exchange. Knowledge regarding the structure and process of a team is described in a language called MALLET. Beliefs about shared team processes and their states are represented using Petri Nets. Based on this model, CAST agents offer information proactively to those who might need it using an algorithm called DIARG. Empirical evaluations using a multi-agent synthetic testbed application indicate that CAST enhances the effectiveness of teamwork among agents without sacrificing a high cost for communications.

Original languageEnglish (US)
Pages (from-to)1135-1142
Number of pages8
JournalIJCAI International Joint Conference on Artificial Intelligence
StatePublished - 2001
Event17th International Joint Conference on Artificial Intelligence, IJCAI 2001 - Seattle, WA, United States
Duration: Aug 4 2001Aug 10 2001

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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