Spar: A Planner That Satisfies Operational and Geometric Goals in Uncertain Environments

A prerequisite for intelligent behavior is the ability to reason about actions and their effects. This ability is the essence of the classical AI planning problem in which plans are constructed by reasoning about how available actions can be applied to achieve various goals. For this reasoning process to occur, the planner must be aware of its available actions, the situations in which they are applicable, and the changes affected in the world by their execution. Classical AI planners typically use a highlevel, symbolic representation of actions (for example, well-formed formulas from predicate calculus). Although this type of representational scheme is attractive from a computational standpoint, it cannot adequately represent the intricacies of a domain that includes complex actions, such as robotic assembly (consider, for example, that any geometric configuration of the robotic manipulator is a rather complex function of six joint angles).