Algorithms for clustering data
Algorithms for clustering data
Technical Note: \cal Q-Learning
Machine Learning
Case-based reasoning
A polynomial time computable metric between point sets
Acta Informatica
Operational specification of a commitment-based agent communication language
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
An approach to the analysis and design of multiagent systems based on interaction frames
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
The state of the art in agent communication languages
Knowledge and Information Systems
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
A Social Semantics for Agent Communication Languages
Issues in Agent Communication
Distance Induction in First Order Logic
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
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
Communication in Multiagent Systems
Communication in Multiagent Systems
Empirical-Rational Semantics of Agent Communication
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Hierarchical Reinforcement Learning in Communication-Mediated Multiagent Coordination
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Acquiring and adapting probabilistic models of agent conversation
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
An empirical semantics approach to reasoning about communication
Engineering Applications of Artificial Intelligence
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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 adaptivenessin agent communication. We present a particular approach that combines conversation patternsas a generic way of describing the available means of communication in a MAS with a decisiontheoretic framework and various different machine learning techniques for applyingthese patterns in and adaptingthem from actual conversations.