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Summary: Planning for Interactions among Autonomous
Agents
Tsz-Chiu Au, Ugur Kuter, and Dana Nau
University of Maryland, College Park, MD 20742, USA
Abstract. AI planning research has traditionally focused on offline plan-
ning for static single-agent environments. In environments where an
agent needs to plan its interactions with other autonomous agents, plan-
ning 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.
1 Introduction
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 complex
computationally: the actions of the other agents can induce a combinatorial
explosion in the number of contingencies that the planner will need to consider,
making both the search space and the solution size exponentially larger.
This paper discusses several techniques for reducing the computational com-
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