Actions, Causation and Logic Programming

Abstract

Reasoning about changes caused by the execution of actions has long been at the center of attention of researchers in the area of logic-based AI. Logical properties of causal dependencies turned out to be similar to properties of rules in logic programs. This fact allows us to apply methods of logic programming to computational problems related to action and change. Ideas of answer set programming, based on the concept of a stable model, turned out to be particularly useful. In the past they have been applied primarily to the problem of plan generation. There is now increasing interest also in using logic programming for learning action descriptions.