Despite the success of the BDI approach to agent teamwork, initial role allocation (i.e. deciding which agents to allocate to key roles in the team) and role reallocation upon failure remain open challenges. What remain missing are analysis techniques to aid human developers in quantitatively comparing different initial role allocations and competing role reallocation algorithms. To remedy this problem, this paper makes three key contributions. First, the paper introduces RMTDP (Role-based Multiagent Team Decision Problem), an extension to MTDP , for quantitative evaluations of role allocation and reallocation approaches. Second, the paper illustrates an RMTDP-based methodology for not only comparing two competing algorithms for role reallocation, but also for identifying the types of domains where each algorithm is suboptimal, how much each algorithm can be improved and at what computational cost (complexity). Such algorithmic improvements are identified via a new automated procedure that generates a family of locally optimal policies for comparative evaluations. Third, since there are combinatorially many initial role allocations, evaluating each in RMTDP to identify the best is extremely difficult. Therefore, we introduce methods to exploit task decompositions among subteams to significantly prune the search space of initial role allocations. We present experimental results from two distinct domains
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