AI planning research has traditionally focused on offline planning for static single-agent environments. In environments where an agent needs to plan its interactions with other autonomous agents, planning is much more complicated, because the actions of the other agents can induce a combinatorial explosion in the number of contingencies that the planner will need to consider. This paper discusses several ways to alleviate the combinatorial explosion, and illustrates their use in several different kinds of multi-agent planning domains.
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