Technical Note: \cal Q-Learning
Machine Learning
Distributed Algorithms
Rational Communication in Multi-Agent Environments
Autonomous Agents and Multi-Agent Systems
Optimizing information exchange in cooperative multi-agent systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
Cooperative information sharing to improve distributed learning in multi-agent systems
Journal of Artificial Intelligence Research
Multi-agent Cooperative Planning and Information Gathering
CIA '07 Proceedings of the 11th international workshop on Cooperative Information Agents XI
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part II
Self-configuration via cooperative social behavior
ATC'06 Proceedings of the Third international conference on Autonomic and Trusted Computing
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A major challenge in efficiently solving distributed resource allocation problems is to cope with the dynamic state changes that characterise such systems. An effective solution to this problem should be able to detect state changes and determine why they occur (diagnosing the cause) in order to adapt to the prevailing situation. Now, since agents typically have localised views and communication constraints that prohibit global instantaneous synchronisation, we argue that cooperative information-sharing can provide them with the necessary adaptiveness and diagnostics ability. To this end, we develop a novel information-sharing algorithm for resource allocation tasks by building upon the most effective algorithm currently available in this domain. Then, using empirical analyses on a resource allocation application with dynamic state changes, network call routing with network failures, we show that, compared to the benchmark, our new algorithm achieves up to a 20% increase in call throughput, up to 3.5 times faster throughput recovery after failures, and provides a novel mechanism for distributed failure diagnosis without false positives and false negatives.