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Multi-Agent Task Assignment in the Bandit Framework | IEEE Conference Publication | IEEE Xplore

Multi-Agent Task Assignment in the Bandit Framework


Abstract:

We consider a task assignment problem for a fleet of UAVs in a surveillance/search mission. We formulate the problem as a restless bandits problem with switching costs an...Show More

Abstract:

We consider a task assignment problem for a fleet of UAVs in a surveillance/search mission. We formulate the problem as a restless bandits problem with switching costs and discounted rewards: there are N sites to inspect, each one of them evolving as a Markov chain, with different transition probabilities if the site is inspected or not. The sites evolve independently of each other, there are transition costs cij for moving between sites i and j∈ {1,..., N}, rewards when visiting the sites, and we maximize a mixed objective function of these costs and rewards. This problem is known to be PSPACE-hard. We present a systematic method, inspired from the work of Bertsimas and Niño-Mora [1] on restless bandits, for deriving a linear programming relaxation for such locally decomposable MDPs. The relaxation is computable in polynomial-time offline, provides a bound on the achievable performance, as well as an approximation of the cost-to-go which can be used online in conjunction with standard suboptimal stochastic control methods. In particular, the one-step lookahead policy based on this approximate cost-to-go reduces to computing the optimal value of a linear assignment problem of size N. We present numerical experiments, for which we assess the quality of the heuristics using the performance bound.
Date of Conference: 13-15 December 2006
Date Added to IEEE Xplore: 07 May 2007
Print ISBN:1-4244-0171-2
Print ISSN: 0191-2216
Conference Location: San Diego, CA, USA

References

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