Acquiring and Adapting Probabilistic Models of Agent Conversation

Communication in multiagent 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.As one example for such a model, interaction frames follow an empirical semantics view of communication, where meaning is defined in terms of expected consequences, and allow for a combination of existing expectations with empirical observation of how communication is used in practice.In this paper, we use methods from the fields of case-based reasoning, inductive logic programming and cluster analysis to devise a formal scheme for the acquisition and adaptation of interaction frames from actual conversations, enabling agents to autonomously (i.e. independent of users and system designers) create and maintain a concise model of the different classes of conversation in a MAS on the basis of an initial set of ACL and protocol specifications. This resembles the first rigorous attempt to solve this problem that is decisive for building truly autonomous agents.