@article {1509590,
	title = {Hybrid BDI-POMDP Framework for Multiagent Teaming },
	journal = {Journal of AI Research (JAIR)},
	volume = {23},
	year = {2005},
	pages = {367-420},
	abstract = {Many current large-scale multiagent team implementations can be characterized as
following the {\textquotedblleft}belief-desire-intention{\textquotedblright} (BDI) paradigm, with explicit representation of team
plans. Despite their promise, current BDI team approaches lack tools for quantitative
performance analysis under uncertainty. Distributed partially observable Markov decision
problems (POMDPs) are well suited for such analysis, but the complexity of finding optimal
policies in such models is highly intractable. The key contribution of this article is a
hybrid BDI-POMDP approach, where BDI team plans are exploited to improve POMDP
tractability and POMDP analysis improves BDI team plan performance.
Concretely, we focus on role allocation, a fundamental problem in BDI teams: which
agents to allocate to the different roles in the team. The article provides three key contributions. First, we describe a role allocation technique that takes into account future
uncertainties in the domain; prior work in multiagent role allocation has failed to address
such uncertainties. To that end, we introduce RMTDP (Role-based Markov Team Decision Problem), a new distributed POMDP model for analysis of role allocations. Our
technique gains in tractability by significantly curtailing RMTDP policy search; in particular, BDI team plans provide incomplete RMTDP policies, and the RMTDP policy search
fills the gaps in such incomplete policies by searching for the best role allocation. Our
second key contribution is a novel decomposition technique to further improve RMTDP
policy search efficiency. Even though limited to searching role allocations, there are still
combinatorially many role allocations, and evaluating each in RMTDP to identify the best
is extremely difficult. Our decomposition technique exploits the structure in the BDI team
plans to significantly prune the search space of 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.},
	author = {Ranjit Nair and Tambe, Milind}
}
