Technical Note: \cal Q-Learning
Machine Learning
Case-based reasoning
Expectation-Oriented Analysis and Design
AOSE '01 Revised Papers and Invited Contributions from the Second International Workshop on Agent-Oriented Software Engineering II
Learning models of intelligent agents
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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
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
Adaptivity at every layer: a modular approach for evolving societies of learning autonomous systems
Proceedings of the 2008 international workshop on Software engineering for adaptive and self-managing systems
Adaptiveness in Agent Communication: Application and Adaptation of Conversation Patterns
Agent Communication II
A Generic Framework for Argumentation-Based Negotiation
CIA '07 Proceedings of the 11th international workshop on Cooperative Information Agents XI
An empirical semantics approach to reasoning about communication
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Multiagent learning for open systems: a study in opponent classification
Adaptive agents and multi-agent systems
Practical strategic reasoning and adaptation in rational argument-based negotiation
ArgMAS'05 Proceedings of the Second international conference on Argumentation in Multi-Agent Systems
A classification framework of adaptation in multi-agent systems
CIA'06 Proceedings of the 10th international conference on Cooperative Information Agents
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This paper introduces InFFrA, a novel method for the analysis and design of multiagent systems that is based on the notions of interaction frames and framing. We lay out a conceptual framework for viewing multiagent systems (MAS) as societies consisting of socially intelligent agents that record and organise their interaction experience so as to use it strategically in future interactions. We also provide criteria for the class of MAS InFFrA is suited for. The benefits of our approach are that it helps to understand and develop socially intelligent agents as well as to identify shortcomings of existing MAS. The method is evaluated through the analysis of an opponent classification heuristic that is used to optimise strategic behaviour in multiagent games, and interesting issues for future research are discussed.