Recognizing Multi-Agent Plans When Action Models and Team Plans Are Both Incomplete

Multi-Agent Plan Recognition (MAPR) aims to recognize team structures (which are composed of team plans) from the observed team traces (action sequences) of a set of intelligent agents. In this article, we introduce the problem formulation of MAPR based on partially observed team traces, and present a weighted MAX-SAT–based framework to recognize multi-agent plans from partially observed team traces with the help of two types of auxiliary knowledge to help recognize multi-agent plans, i.e., a library of incomplete team plans and a set of incomplete action models. Our framework functions with two phases. We first build a set of hard constraints that encode the correctness property of the team plans, and a set of soft constraints that encode the optimal utility property of team plans based on the input team trace, incomplete team plans, and incomplete action models. After that, we solve all of the constraints using a weighted MAX-SAT solver and convert the solution to a set of team plans that best explain the structure of the observed team trace. We empirically exhibit both effectiveness and efficiency of our framework in benchmark domains from International Planning Competition (IPC).

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