Learning to Communicate in Decentralized Systems

Martin Allen, Claudia V. Goldman, and Shlomo Zilberstein. Learning to Communicate in Decentralized Systems. Proceedings of the Eighth Binennial Israeli Symposium on the Foundations of AI (BISFAI), Haifa, Israel, 2005.

Abstract

Learning to communicate is an emerging challenge in AI research. It is known that agents interacting in decentralized, stochastic environments can benefit from exchanging information. Multiagent planning generally assumes that agents share a common means of communication; however, in building robust distributed systems it is important to address potential miscoordination resulting from misinterpretation of messages exchanged. This paper lays foundations for studying this problem, examining its properties analytically and empirically in a decision-theoretic context. Solving the problem optimally is often intractable, but our approach enables agents using different languages to converge upon coordination over time.

Bibtex entry:

@inproceedings{AGZbisfai05,
  author	= {Martin Allen and Claudia V. Goldman and Shlomo Zilberstein},
  title		= {Learning to Communicate in Decentralized Systems},
  booktitle     = {Proceedings of the Eighth Binennial Israeli Symposium on the 
                   Foundations of AI},
  year		= {2005},
  pages		= {},
  address       = {Haifa, Israel},
  url		= {http://rbr.cs.umass.edu/shlomo/papers/AGZbisfai05.html}
}

shlomo@cs.umass.edu