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Action planning deals with the problem of finding a sequence of actions transferring the world from a given state to a desired (goal) state. This problem is important in various areas such as robotics, manufacturing, transportation, autonomic computing, computer games, etc. Action planning is a form of a reachability problem in a huge state space so it is critical to efficiently represent world states and actions (transitions between states). In this paper we present a modeling framework for planning problems based on tabled logic programming that exploits a planner module in the Picat language. In particular, we suggest techniques for structured representation of states and for including control knowledge in the description of actions. We demonstrate these techniques using the complex planning domain Cave Diving from the International Planning Competition. Experimentally, we show properties of the model for different search approaches and we compare the performance of the proposed approach with state-of-the-art automated planners. The focus of this paper is on providing guidelines for manual modeling of planning domains rather than on automated reformulation of models.

Knowledge engineering for automated planning is still in its childhood and there has been little work done on how to model planning problems. The prevailing approach in the academic community is using the PDDL language that originated in planning competitions. In contrast, real applications require more modeling flexibility and different modeling languages were designed in order to allow efficient planning. This paper focuses on the role of a domain modeling formalism as an interface between a domain modeler and a planner.