A Hardware Relaxation Paradigm for Solving NP-Hard Problems
Planning is a notoriously hard combinatorial search problem. In many interesting domains, current planning algorithms fail to scale up gracefully. By combining a general, stochastic search algorithm and appropriate problem encodings based on propositional logic, we are able to solve hard planning problems many times faster than the best current planning systems. Although stochastic methods have been shown to be very e ective on a wide range of scheduling problems, this is the rst demonstration of its power on truly challenging classical planning instances. This work also provides a new perspective on representational issues in planning.