Learning to Communicate in Decentralized Systems
Martin Allen
Claudia V. Goldman
Shlomo 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
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.
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