Extending PDDL for Hierarchical Planning and Topological Abstraction
Adi Botea, Jonathan Schaeffer · 2003
Despite major progress in AI planning over the last few years, many interesting domains remain challenging for cur-rent planners. Topological abstraction can reduce planning complexity in several domains, decomposing a problem into a two-level hierarchy. This paper presents LAP, a planning model based on topological abstraction. In formalizing LAP as a generic planning framework, the support of a planning language more expressive than PDDL can be very important. We discuss how an extended version of PDDL can be part of our planning framework, by providing support for hierar-chical planning and topological abstraction. We demonstrate our ideas in Sokoban and path-finding, two domains where topological abstraction is useful.