Cost-Optimal Factored Planning: Promises and Pitfalls

Factored planning methods aim to exploit locality to efficiently solve large but "loosely coupled" planning problems by computing solutions locally and propagating limited information between components. However, all factored planning methods presented so far work with representations that require certain parameters to be bounded (e.g. number of coordination points between local plans considered); the satisfaction of those bounds by a given problem instance is difficult to establish a priori, and the influence of those parameters on the problem complexity is unclear. We present an instance of the factored planning framework using a representation of the (regular) sets of local plans by finite automata, which does not require any such bound. By substituting weighted automata, we can even do factored cost-optimal planning. We test an implementation of the method on the few standard planning benchmarks that we have found to be amenable to factoring. We show that this method run...

• Type: Conference
• Year: 2010
• Where: AIPS
• Authors: Eric Fabre, Loig Jezequel, Patrik Haslum, Sylvie Thiébaux
• Keywords: AIPS 2010, Artificial Intelligence, Factored Planning, Factored Planning Framework, Factored Planning Methods