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A key challenge in non-cooperative multi-agent systems is that of developingefficient planning algorithms for intelligent agents to interact and performeffectively among boundedly rational, self-interested agents (e.g., humans).The practicality of existing works addressing this challenge is beingundermined due to either the restrictive assumptions of the other agents'behavior, the failure in accounting for their rationality, or the prohibitivelyexpensive cost of modeling and predicting their intentions. To boost thepracticality of research in this field, we investigate how intention predictioncan be efficiently exploited and made practical in planning, thereby leading toefficient intention-aware planning frameworks capable of predicting theintentions of other agents and acting optimally with respect to their predictedintentions. We show that the performance losses incurred by the resultingplanning policies are linearly bounded by the error of intention prediction.Empirical evaluations through a series of stochastic games demonstrate that ourpolicies can achieve better and more robust performance than thestate-of-the-art algorithms.

Interactive POMDP Lite: Towards Practical Planning to Predict and Exploit Intentions for Interacting with Self-Interested Agents