Using partial global plans to coordinate distributed problem solvers
Distributed Artificial Intelligence
Collaborative plans for complex group action
Artificial Intelligence
Opportunistic multimodel diagnosis with imperfect models
Information Sciences: an International Journal
KQML as an agent communication language
Software agents
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Mutual online concept learning for multiple agents
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
Toward Automating Evolution of Agent Communication Languages
HICSS '02 Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 3 - Volume 3
Optimizing information exchange in cooperative multi-agent systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
The communicative multiagent team decision problem: analyzing teamwork theories and models
Journal of Artificial Intelligence Research
Learning to communicate in a decentralized environment
Autonomous Agents and Multi-Agent Systems
Shaping multi-agent systems with gradient reinforcement learning
Autonomous Agents and Multi-Agent Systems
Decentralized control of cooperative systems: categorization and complexity analysis
Journal of Artificial Intelligence Research
Language learning in multi-agent systems
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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This paper presents an algorithm for learning the meaning of messages communicated between agents that interact while acting optimally towards a cooperative goal. Our reinforcement-learning method is based on Bayesian filtering and has been adapted for a decentralized control process. Empirical results shed light on the complexity of the learning problem, and on factors affecting the speed of convergence. Designing intelligent agents able to adapt their mutual interpretation of messages exchanged, in order to improve overall task-oriented performance, introduces an essential cognitive capability that can upgrade the current state of the art in multi-agent and human-machine systems to the next level. Learning to communicate while acting will add to the robustness and flexibility of these systems and hence to a more efficient and productive performance.