Communication in multi-agent systems (MASs) is usually governed by agent communication languages (ACLs) and communication protocols carrying a clear cut semantics. With an increasing degree of openness, however, the need arises for more flexible models of communication that can handle the uncertainty associated with the fact that adherence to a supposedly agreed specification of possible conversations cannot be ensured on the side of other agents. In this paper, we argue for adaptiveness in agent communication. We present a particular approach that combines conversation patterns as a generic way of describing the available means of communication in a MAS with a decision-theoretic framework and various different machine learning techniques for applying these patterns in and adapting them from actual conversations.
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