• Sorted by Date • Classified by Publication Type • Sorted by First Author Last Name • Classified by Research Category •
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 Third International Workshop on Agent Technology for Sensor Networks (at AAMAS-09), May 2009.
ATSN-2009
Superseded by the IJCAI-09 conference paper DCOPs
Meet the Real World: Exploring Unknown Reward Matrices with Applications to Mobile Sensor Networks.
(unavailable)
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(ATSN09-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 Third International Workshop on Agent Technology for Sensor Networks (at AAMAS-09)", month="May", year= "2009", wwwnote={<a href="http://www.atsn09.org">ATSN-2009</a><br>Superseded by the IJCAI-09 conference paper <a href="http://cs.lafayette.edu/~taylorm/Publications/b2hd-IJCAI09-Jain.html">DCOPs Meet the Real World: Exploring Unknown Reward Matrices with Applications to Mobile Sensor Networks</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.}, )
Generated by bib2html.pl (written by Patrick Riley ) on Thu Jul 24, 2014 16:09:11