Learning to Communicate in a Decentralized Environment
Claudia V. Goldman
Martin Allen
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. Multi-agent 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.
We establish a formal framework for the problem, and identify a collection
of necessary and sufficient properties for decision problems that allow
agents to employ probabilistic updating schemes in order to learn how to
interpret what others are communicating. Solving the problem optimally is
often intractable, but our approach enables agents using different
languages to converge upon coordination over time. Our experimental work
establishes how these methods perform when applied to problems of varying
complexity.
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