We consider the problem of solving qualitative decentralized partially observable Markov decision processes (QDec-POMDPs) with deterministic actions. QDec-POMDPs model dynamic systems consisting of a collaborative team of agents acting under uncertainty and partial observability, attempting to reach a desirable goal state. They can be viewed as the multi-agent version of the contingent planning model. In this work, we extend the idea of factored planning from fully observable multi-agent planning to partial observability. Our method operates as follows: First, we simplify the multi-agent planning (MAP) problem by reducing it to a single-agent planning problem. Then, we use the solution to this single-agent problem as a skeleton plan, that each agent attempts to complete separately. We describe different variants of this idea, and in particular, suggest a method that models information about each agent’s knowledge and incorporates the idea of signaling information to other agents through actions. We perform an extensive empirical evaluation over new and old domains, demonstrating the enhanced scalability of our method.
Shekhar et al. (Sun,) studied this question.