Using partial global plans to coordinate distributed problem solvers
Distributed Artificial Intelligence
The contract net protocol: high-level communication and control in a distributed problem solver
Distributed Artificial Intelligence
Learning in embedded systems
An adaptive communication protocol for cooperating mobile robots
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Emergent coordination through the use of cooperative state-changing rules
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Communication in reactive multiagent robotic systems
Autonomous Robots
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
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Coordination of Distributed Problem Solvers
Coordination of Distributed Problem Solvers
Mutual online concept learning for multiple agents
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
Sequential Optimality and Coordination in Multiagent Systems
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
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
Optimizing the mutual intelligibility of linguistic agents in a shared world
Artificial Intelligence
Comparing formal theories of context in AI
Artificial Intelligence
Decentralized Language Learning through Acting
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Decentralized control of cooperative systems: categorization and complexity analysis
Journal of Artificial Intelligence Research
Solving transition independent decentralized Markov decision processes
Journal of Artificial Intelligence Research
A framework for sequential planning in multi-agent settings
Journal of Artificial Intelligence Research
Artificial Intelligence Review
Optimal and approximate Q-value functions for decentralized POMDPs
Journal of Artificial Intelligence Research
Comparison of tightly and loosely coupled decision paradigms in multiagent expedition
International Journal of Approximate Reasoning
Online planning for multi-agent systems with bounded communication
Artificial Intelligence
A simple metric for turn-taking in emergent communication
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Rewards for pairs of Q-learning agents conducive to turn-taking in medium-access games
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
QueryPOMDP: POMDP-based communication in multiagent systems
EUMAS'11 Proceedings of the 9th European conference on Multi-Agent Systems
Learning Communication in Interactive Dynamic Influence Diagrams
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Incremental clustering and expansion for faster optimal planning in decentralized POMDPs
Journal of Artificial Intelligence Research
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Learning to communicate is an emerging challenge in AI research. It is known that agents interacting in decentralized, stochastic environments can benefit from exchanging information. Multi-agent planning generally assumes that agents share a common means of communication; however, in building robust distributed systems it is important to address potential miscoordination resulting from misinterpretation of messages exchanged. This paper lays foundations for studying this problem, examining its properties analytically and empirically in a decision-theoretic context. We establish a formal framework for the problem, and identify a collection of necessary and sufficient properties for decision problems that allow agents to employ probabilistic updating schemes in order to learn how to interpret what others are communicating. Solving the problem optimally is often intractable, but our approach enables agents using different languages to converge upon coordination over time. Our experimental work establishes how these methods perform when applied to problems of varying complexity.