GPT: A Tool for Planning with Uncertainty and Partial Information

We describe the GPT system and its utilization over a number of examples. GPT (General Planning Tool) is an integrated software tool for modeling, analyzing and solving a wide range of planning problems dealing with uncertainty and partial information, that has been used for us and others for research and teaching. Our approach is based on different state models that can handle various types of action dynamics (deterministic and probabilistic) and sensor feedback (null, partial, and complete). The system consists mainly of a high-level language for expressing actions, sensors, and goals, and a bundle algorithms based on heuristic search for solving them. The language is one of GPT’s strengths since it presents the user a consistent and unified framework for the planning task. These descriptions are then solved by appropriate algorithms chosen from the bundle. The output for all models is a plan that is ready for execution and analysis; moreover, for a class of problems described below, it is an optimal plan. The rest of the paper is organized as follows. In next two sections, we describe the mathematical models that formalize the tasks GPT is able to solve, and the algorithms used to solve them. In Sect. 4, we show GPT’s language with an example and give an overview of its syntax. At the end, Sect. 6 shows results over selected examples from different classes of problems; it contains actual input and output to GPT as codification of problems in the language and the resulting plans generated by GPT.