Full-text

URL: http://rbr.cs.umass.edu/~mwallen/docs/aaai05_wksp.pdf

Cached: PDF-143K

SAVE AS an easy-to-recall long filename:

Filename format: author--year--title   PDF-143K

Filename format: author--year--title--journal|proceedings|...--pages   PDF-143K

Related links

Source: http://rbr.cs.umass.edu/~mwallen/Publications.html

Web search: Google Web Search   ::   Google Scholar

Within this site: References (22)

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 mis-coordination 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.

@inproceedings{allen05learningToCommunicate,
  author={Martin Allen and Claudia V. Goldman and Shlomo Zilberstein},
  title={Learning to Communicate in Decentralized Systems},
  year={2005},
  pages={1--8},
  address={Pittsburgh, PA},
  booktitle={Proceedings of the Workshop on Multiagent Learning, AAAI-05},
  url={http://www.isrl.uiuc.edu/~amag/langev/paper/allen05learningToCommunicate.html}
}

HOME   ::  Conference List   ::   Conference Paper	Comments to: junwang4 you-know-at gmail.com	Last update: 2/2/08