Hybrid BDI-POMDP Framework for Multiagent Teaming
R. Nair, M. Tambe

TL;DR
This paper introduces a hybrid BDI-POMDP framework that enhances multiagent team planning by integrating BDI plans with POMDP analysis, focusing on role allocation under uncertainty to improve efficiency and performance.
Contribution
It presents a novel hybrid approach combining BDI and POMDP methods, including a new RMTDP model, a decomposition technique for role allocation, and a faster policy evaluation algorithm.
Findings
The hybrid approach improves role allocation under uncertainty.
The RMTDP model enhances analysis of multiagent team plans.
Decomposition significantly reduces search space for role assignments.
Abstract
Many current large-scale multiagent team implementations can be characterized as following the belief-desire-intention (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…
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