Modeling Planning Problems Using Constraint Satisfaction

Planning problems deal with finding a sequence of actions that transfer the initial state of the world into a desired state. Frequently such problems are solved by dedicated algorithms but there exist planners based on translating the planning problem into a different formalism such as constraint satisfaction or Boolean satisfiability and using a general solver for this formalism. This paper describes the approach to modeling planning as constraint satisfaction and shows how to enhance the existing constraint models of planning problems by using techniques such as symmetry breaking (dominance rules), singleton consistency, nogood learning, and lifting.