Reasoning about the effects of actions in automated planning systems

This paper describes a temporal reasoning system that supports deductions for modeling the physics (i.e. cause and effect relationships) of a specified planning domain. It demonstrates how the process of planning can be profitably partitioned into two inferential components: one responsible for making choices relevant to the construction of a plan and a second responsible for maintaining an accurate picture of the future that takes into account the planner’s intended actions. Causal knowledge about the effects of actions and the behavior of processes is stored apart from knowledge of plans for achieving specific tasks. Using this causal knowledge, the second component is able to predict the consequences of actions proposed by the first component and notice interactions that may affect the success of the plan under construction. By keeping track of the reasons why each prediction and choice is made, the resulting system is able to reason efficiently about the consequences of making new choices and retracting old ones. The system described in this paper makes it particularly simple and efficient to reason about actions whose effects vary depending upon the circumstances in which the actions are executed.