@mastersthesis {1510014,
	title = {Coordinating multiagent teams in uncertain domains using distributed POMDPs },
	year = {2004},
	type = {PhD thesis},
	abstract = {Distributed Partially Observable Markov Decision Problems (POMDPs) have
emerged as a popular decision-theoretic approach for planning for multiagent
teams, where it is imperative for the agents to be able to reason about the
rewards (and costs) for their actions in the presence of uncertainty. However,
finding the optimal distributed POMDP policy is computationally intractable
(NEXP-Complete). This dissertation presents two independent approaches which
deal with this issue of intractability in distributed POMDPs. The primary focus is on the first approach, which represents a principled way to combine the
two dominant paradigms for building multiagent team plans, namely the {\textquotedblleft}beliefdesire-intention{\textquotedblright} (BDI) approach and distributed POMDPs. In this hybrid BDIPOMDP approach, BDI team plans are exploited to improve distributed POMDP
tractability and distributed POMDP-based analysis improves BDI team plan performance. Concretely, we focus on role allocation, a fundamental problem in BDI
teams {\textendash} which agents to allocate to the different roles in the team. The hybrid
BDI-POMDP approach provides three key contributions. First, unlike prior work in multiagent role allocation, we describe a role allocation technique that takes
into account future uncertainties in the domain. The second contribution is a novel
decomposition technique, which exploits the structure in the BDI team plans to
significantly prune the search space of combinatorially many role allocations. Our
third key contribution is a significantly faster policy evaluation algorithm suited
for our BDI-POMDP hybrid approach. Finally, we also present experimental
results from two domains: mission rehearsal simulation and RoboCupRescue disaster rescue simulation. In the RoboCupRescue domain, we show that the role
allocation technique presented in this dissertation is capable of performing at human expert levels by comparing with the allocations chosen by humans in the
actual RoboCupRescue simulation environment. The second approach for dealing
with the intractability of distributed POMDPs is based on finding locally optimal
joint policies using Nash equilibrium as a solution concept. Through the introduction of communication, we not only show improved coordination but also develop
a novel compact policy representation that results in savings of both space and
time which are verified empirically.},
	author = {Ranjit Nair}
}
