Learning to communicate in decentralized systems
M. Allen
C. V. Goldman
S. Zilberstein
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{Allen05b,
author = {Martin Allen and Claudia V. Goldman and Shlomo Zilberstein},
title = {Learning to Communicate in Decentralized Systems},
booktitle = {Proceedings of the Workshop on Multiagent Learning, Twentieth National
Conference on Artificial Intelligence ({AAAI}-05)},
pages = {1--8},
year = 2005,
address = {Pittsburgh, PA},
note = {AAAI Tech.\ Report WS-05-09}
}