Integrating Belief-Desire-Intention Approaches with POMDPS: The Case of Team-Oriented Programs

Ranjit Nair, Milind Tambe, and Stacy Marsella

Integrating approaches based on belief-desire-intentions (BDI) logics with the more recent developments of distributed POMDPs is today a fundamental challenge in the multiagent systems arena. One common suggestion for such an integration is to use stochastic models (POMDPs) for generating agent behaviors, while using the BDI components for monitoring and creating explanations. We propose a completely inverse approach, where the BDI components are used to generate agent behaviors, and distributed POMDPs are used in an analysis mode. In particular, we focus on teamoriented programs for tasking multiagent teams, where the team-oriented programs specify hierarchies of team plans that the team and its subteams must adopt as their joint intentions. However, given a limited number of agents, finding a good way to allocate them to different teams and subteams to execute such a team-oriented program is a difficult challenge We use distributed POMDPs to analyze different allocations of agents within a team-oriented program, and to suggest improvements to the program. The key innovation is to use the distributed POMDP analysis not as a black box, but as a glass box, offering insights into why particular allocations lead to good or bad outcomes. These insights help to prune the search space of different allocations, offering significant speedups in the search. We present preliminary experimental results to illustrate our methodology.


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