The Nature of Knowledge in an Abductive Event Calculus Planner

There are several works whose goal is to specify complete and sound planning systems based on general purpose theorem provers. Some planners implemented in this way can have a close correspondence with existing partial-ordered planning algorithms. To improve the efficiency of logic-based planners we would like to use some of the results achieved by the AI planning community over the past twenty years in terms of algorithm design. We claim that a knowledge level analysis of problem-solving methods for planning, can help to identify what is the role of each piece of knowledge in a system and provide a common language to map, classify and compare different systems. In this paper we analyze an abductive event calculus planner using a library of problem-solving methods for planning.