@conference {1508527,
	title = {Not All Agents Are Equal: Scaling up Distributed POMDPs for Agent Networks },
	booktitle = {The Seventh International Conference on Autonomous Agents and Multiagent Systems},
	year = {2008},
	abstract = {Many applications of networks of agents, including mobile sensor networks, unmanned air vehicles, autonomous underwater vehicles, involve 100s of agents acting collaboratively under uncertainty. Distributed Partially Observable Markov Decision Problems (Distributed POMDPs) are well-suited to address such applications, but so far, only limited scale-ups of up to five agents
have been demonstrated. This paper escalates the scale-up, presenting an algorithm called FANS, increasing the number of agents
in distributed POMDPs for the first time into double digits. FANS
is founded on finite state machines (FSMs) for policy representation and expoits these FSMs to provide three key contributions:
(i) Not all agents within an agent network need the same expressivity of policy representation; FANS introduces novel heuristics
to automatically vary the FSM size in different agents for scaleup; (ii) FANS illustrates efficient integration of its FSM-based policy search within algorithms that exploit agent network structure;
(iii) FANS provides significant speedups in policy evaluation and
heuristic computations within the network algorithms by exploiting the FSMs for dynamic programming. Experimental results
show not only orders of magnitude improvements over previous
best known algorithms for smaller-scale domains (with similar solution quality), but also a scale-up into double digits in terms of
numbers of agents.},
	author = {Janusz Marecki and Tapana Gupta and Varakantham, Pradeep and Tambe, Milind and Makoto Yokoo}
}
