Interaction is meaning: a new model for communication in open systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Temporal abstraction in reinforcement learning
Temporal abstraction in reinforcement learning
Advances in Agent Communication: International Workshop on Agent Communication Languages, Acl 2003, Melbourne, Australia, July 14, 2003: Revised and Invited Papers (Lecture Notes in Computer Science, 2922.)
Acquiring and adapting probabilistic models of agent conversation
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Agent Behavior Alignment: A Mechanism to Overcome Problems in Agent Interactions During Runtime
CIA '07 Proceedings of the 11th international workshop on Cooperative Information Agents XI
Performative patterns for designing verifiable ACLs
CIA'06 Proceedings of the 10th international conference on Cooperative Information Agents
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We present an integrated approach for reasoning about and learning conversation patterns in multiagent communication. The approach is based on the assumption that information about the communication language and protocols available in a multiagent system is provided in the form of dialogue sequence patterns, possibly tagged with logical conditions and instance information. We describe an integrated social reasoning architecture m2InFFrA that is capable of (i) processing such patterns, (ii) making communication decisions in a boundedly rational way, and (iii) learning patterns and their strategic application from observation.