Decentralized Language Learning Through Acting
Claudia V. Goldman
Martin Allen
Shlomo Zilberstein
Abstract
This paper presents an algorithm for learning the meaning
of messages communicated between agents that interact
while acting optimally towards a cooperative goal. Our
reinforcement-learning method is based on Bayesian filtering
and has been adapted for a decentralized control process.
Empirical results shed light on the complexity of the
learning problem, and on factors affecting the speed of convergence.
Designing intelligent agents able to adapt their
mutual interpretation of messages exchanged, in order to
improve overall task-oriented performance, introduces an
essential cognitive capability that can upgrade the current
state of the art in multi-agent and human-machine systems
to the next level. Learning to communicate while acting will
add to the robustness and flexibility of these systems and
hence to a more efficient and productive performance.
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