There is extreme flexibility in specifying the engines to use. This gives blackbox the capability of functioning efficiently over a broad range of problems. The concise state representation is inferred in an analysis prior to the search and, by utilizing this representation, accurate reachability analysis and backward chaining are carried out without necessarily encountering exponential representation explosion. If problems become harder and information on the solution length is available, Mips invokes its incorporated heuristic single state search and heuristic symbolic search engines with BDDs, thus featuring two entirely different planning algorithms, aimed to assist each other on the same staterepresentation.
System R is based on regression, and solves a goal one at a time.
Briefly, given a conjunctive goal G, it chooses the first subgoal g that has not been satisfied yet in the current state, and work on it. Once it is achieved, say by P, it progresses the current state through P to a new current state, moves g to the end of G, and recursively tries to find a plan for the new G. When working on g, it regresses g over an action to a conjunctive goal G', and tries to achieve G' recursively. This to a large degree can be remedied by using domain specific control information. Using these informations, FF employs a novel local search strategy that combines Hill-climbing with systematic search. HSP2 Hector Geffner Blai Bonet Universidad Simon Bolivar Depto de Computacion HSP2 is an heuristic-search planner that descends from the HSP plannerentered into the AIPS98 Contest.