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Manish Jain, Matthew E. Taylor, Makoto Yokoo, and Milind
Tambe. DCOPs Meet the Real World: Exploring Unknown Reward Matrices with Applications to Mobile Sensor Networks.
In Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI), July 2009. 26%
acceptance rate
IJCAI-2009
Buoyed by recent successes in the area of distributed constraint optimization problems (DCOPs), this paper addresses challenges faced when applying DCOPs to real-world domains. Three fundamental challenges must be addressed for a class of real-world domains, requiring novel DCOP algorithms. First, agents may not know the payoff matrix and must explore the environment to determine rewards associated with variable settings. Second, agents may need to maximize total accumulated reward rather than instantaneous final reward. Third, limited time horizons disallow exhaustive exploration of the environment. We propose and implement a set of novel algorithms that combine decision-theoretic exploration approaches with DCOP-mandated coordination. In addition to simulation results, we implement these algorithms on robots, deploying DCOPs on a distributed mobile sensor network.
@inproceedings(IJCAI09-Jain, author="Manish Jain and Matthew E.\ Taylor and Makoto Yokoo and Milind Tambe", title="{DCOP}s Meet the Real World: Exploring Unknown Reward Matrices with Applications to Mobile Sensor Networks", Booktitle="Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence ({IJCAI})", month="July", year= "2009", note = {26% acceptance rate}, wwwnote={<a href="http://www.ijcai-09.org">IJCAI-2009</a>}, abstract={Buoyed by recent successes in the area of distributed constraint optimization problems (DCOPs), this paper addresses challenges faced when applying DCOPs to real-world domains. Three fundamental challenges must be addressed for a class of real-world domains, requiring novel DCOP algorithms. First, agents may not know the payoff matrix and must explore the environment to determine rewards associated with variable settings. Second, agents may need to maximize total accumulated reward rather than instantaneous final reward. Third, limited time horizons disallow exhaustive exploration of the environment. We propose and implement a set of novel algorithms that combine decision-theoretic exploration approaches with DCOP-mandated coordination. In addition to simulation results, we implement these algorithms on robots, deploying DCOPs on a distributed mobile sensor network.}, )
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