Computational models of planning

The selection of the action to do next is one of the central problems faced by autonomous agents. Natural and artificial systems address this problem in various ways: action responses can be hardwired, they can be learned, or they can be computed from a model of the situation, the actions, and the goals. Planning is the model-based approach to action selection and a fundamental ingredient of intelligent behavior in both humans and machines. Planning, however, is computationally hard as the consideration of all possible courses of action is not computationally feasible. The problem has been addressed by research in Artificial Intelligence that in recent years has uncovered simple but powerful computational principles that make planning feasible. The principles take the form of domain-independent methods for computing heuristics or appraisals that enable the effective generation of goal-directed behavior even over huge spaces. In this paper, we look at several planning models, at methods that have been shown to scale up to large problems, and at what these methods may suggest about the human mind. WIREs Cogn Sci 2013, 4:341-356. doi: 10.1002/wcs.1233 The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website.