@conference {1508842,
	title = {Letting loose a SPIDER on a network of POMDPs: Generating quality guaranteed policies },
	booktitle = {International Conference on Autonomous Agents and Multiagent Systems, AAMAS-2007},
	year = {2007},
	abstract = {Distributed Partially Observable Markov Decision Problems (Distributed POMDPs) are a popular approach for modeling multi-agent
systems acting in uncertain domains. Given the significant complexity of solving distributed POMDPs, particularly as we scale
up the numbers of agents, one popular approach has focused on
approximate solutions. Though this approach is efficient, the algorithms within this approach do not provide any guarantees on solution quality. A second less popular approach focuses on global
optimality, but typical results are available only for two agents,
and also at considerable computational cost. This paper overcomes
the limitations of both these approaches by providing SPIDER, a
novel combination of three key features for policy generation in distributed POMDPs: (i) it exploits agent interaction structure given
a network of agents (i.e. allowing easier scale-up to larger number
of agents); (ii) it uses a combination of heuristics to speedup policy
search; and (iii) it allows quality guaranteed approximations, allowing a systematic tradeoff of solution quality for time. Experimental results show orders of magnitude improvement in performance
when compared with previous global optimal algorithms.},
	author = {Varakantham, Pradeep and Janusz Marecki and Yuichi Yabu and Tambe, Milind and Makoto Yokoo}
}
