Abstract A key challenge in non-cooperative multi-agent systems is that of developing ef?cient planning algorithms for intelligent agents to interact and perform effectively among boundedly rational, selfinterested agents (e.g., humans). The practicality of existing works addressing this challenge is being undermined due to either the restrictive assumptions of the other agents’ behavior, the failure in accounting for their rationality, or the prohibitively expensive cost of modeling and predicting their intentions. To boost the practicality of research in this ?eld, we investigate how intention prediction can be ef?ciently exploited and made practical in planning, thereby leading to ef?cient intention-aware planning frameworks capable of predicting the intentions of other agents and acting optimally with respect to their predicted intentions. We show that the performance losses incurred by the resulting planning policies are linearly bounded by the error of intention prediction. Empirical evaluations through a series of stochastic games demonstrate that our policies can achieve better and more robust performance than the state-of-the-art algorithms.