Logical Encoding Methods in Intelligent Planning

The design and implementation of logical encoding methods are the key issues of translation based planning methods, which need to translate a given planning problem to a series of other classical solvable problems during planning procedures. All the logical encoding methods need to consider the logical representations and reasonings based on the crossponding propositional logic, first-order logic, multi-value logic, probabilistic logic, modal logic, epistemic logic, or the other adopted non-classical logics. This paper introduces the concrete details of the state-of-the-art logical encoding methods in intelligent planning, which include linear encoding, Graphplan based encoding, state based encoding, action based encoding, proposition based encoding, transition based encoding, lifted casual encoding, multi-value variable based encoding, ordered binary decision diagram based encoding, constraint satisfiabilitiy based encoding and so on. It also introduces the possible needed encoding methods of probabilistics, epistemic properties, modal assumptions, and flexible constraints for planning operations or states of some proposed abstract planning domain problems, whose formal characteristic expressions are still disputed. After considering experimental results of International Planning Competition and relevant papers, we conclude their corresponding soundness and possibility, and also application prospects in other relevant areas. Finally, we propose the challenges and possible responding methods, and also possible hotspots of them.