Distributed interpretation: a model and experiment
Distributed Artificial Intelligence
Technical Note: \cal Q-Learning
Machine Learning
Controlling cooperative problem solving in industrial multi-agent systems using joint intentions
Artificial Intelligence
Industrial applications of distributed AI
Communications of the ACM
ARCHON: a distributed artificial intelligence system for industrial application
Foundations of distributed artificial intelligence
T&Aelig;MS: a framework for environment centered analysis and design of coordination mechanisms
Foundations of distributed artificial intelligence
Collaborative plans for complex group action
Artificial Intelligence
COLLAGEN: when agents collaborate with people
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Team-partitioned, opaque-transition reinforcement learning
Proceedings of the third annual conference on Autonomous Agents
Computational organization theory
Multiagent systems
ADEPT: an agent-based approach to business process management
ACM SIGMOD Record
Communication decisions in multi-agent cooperation: model and experiments
Proceedings of the fifth international conference on Autonomous agents
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Coalition Agents Experiment: Multiagent Cooperation in International Coalitions
IEEE Intelligent Systems
Learning to Predict by the Methods of Temporal Differences
Machine Learning
The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
TPOT-RL Applied to Network Routing
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Sequential Optimality and Coordination in Multiagent Systems
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Transition-independent decentralized markov decision processes
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Cooperative negotiation for soft real-time distributed resource allocation
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Minimizing communication cost in a distributed Bayesian network using a decentralized MDP
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Environment centered analysis and design of coordination mechanisms
Environment centered analysis and design of coordination mechanisms
Sharing information for Q-learning-based network bandwidth estimation and network failure detection
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
The AARIA agent architecture: From manufacturing requirements to agent-based system design
Integrated Computer-Aided Engineering
Decentralized supply chain formation: a market protocol and competitive equilibrium analysis
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
Hidden state and reinforcement learning with instance-based stateidentification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Cognitive Systems Research
Sharing information for Q-learning-based network bandwidth estimation and network failure detection
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Collaborative Multiagent Reinforcement Learning by Payoff Propagation
The Journal of Machine Learning Research
A multiagent reinforcement learning algorithm with non-linear dynamics
Journal of Artificial Intelligence Research
Communicating effectively in resource-constrained multi-agent systems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Autonomous agents for self-managed MPLS DiffServ-TE domain
AN'06 Proceedings of the First IFIP TC6 international conference on Autonomic Networking
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Effective coordination of agents' actions in partially-observable domains is a major challenge of multi-agent systems research. To address this, many researchers have developed techniques that allow the agents to make decisions based on estimates of the states and actions of other agents that are typically learnt using some form of machine learning algorithm. Nevertheless, many of these approaches fail to provide an actual means by which the necessary information is made available so that the estimates can be learnt. To this end, we argue that cooperative communication of state information between agents is one such mechanism. However, in a dynamically changing environment, the accuracy and timeliness of this communicated information determine the fidelity of the learned estimates and the usefulness of the actions taken based on these. Given this, we propose a novel information-sharing protocol, post-task-completion sharing, for the distribution of state information. vVe then show, through a formal analysis, the improvement in the quality of estimates produced using our strategy over the widely used protocol of sharing information between nearest neighbours. Moreover, communication heuristics designed around our information-sharing principle are subjected to empirical evaluation along with other benchmark strategies (including Littman's Q-routing and Stone's TPOT-RL) in a simulated call-routing application. These studies, conducted across a range of environmental settings, show that, compared to the different benchmarks used, our strategy generates an improvement of up to 60% in the call connection rate; of more than 1000% in the ability to connect long-distance calls; and incurs as low as 0.25 of the message overhead.