We describe and evaluate the algorithmic techniques that are used in the FF planning system. Like the HSP system, FF relies on forward state space search, using a heuristic that estimates goal distances by ignoring delete lists. Unlike HSP's heuristic, our method does not assume facts to be independent. We introduce a novel search strategy that combines Hill-climbing with systematic search, and we show how other powerful heuristic information can be extracted and used to prune the search space. FF was the most successful automatic planner at the recent AIPS-2000 planning competition. We review the results of the competition, give data for other benchmark domains, and investigate the reasons for the runtime performance of FF compared to HSP.

This paper presents GRT, a domain-independent heuristic planning system for STRIPS worlds. GRT solves

DISCOPLAN is an implemented set of efficient preplanning algorithms intended to enable faster domain-independent planning. It includes algorithms for discovering state constraints (invariants) that have been shown to be very useful, for example, for speeding up SAT-based planning. DISCOPLAN originally discovered only certain types of implicative constraints involving up to two fluent literals and any number of static literals, where one of the fluent literals contains all of the variables occurring in the other literals; only planning domains with STRIPS-like operators were handled. We have now extended DISCOPLAN in several directions. We describe new techniques that handle operators with conditional effects, and enable discovery of several new types of constraints. Moreover, discovered constraints can be fed back into the discovery process to obtain additional constraints. Finally, we outline unimplemented (but provably correct) methods for discovering additional type...

Modern domain-independent heuristic planners evaluate their plans on the basis of their length only. However, in real-world problems there are other criteria that also play an important role, such as resource consumption, profit, safety, etc. This paper extends the GRT planner, an efficient domain-independent heuristic state-space planner, with the ability to consider multiple criteria. The heuristic of GRT is based on the estimation of the distances between each fact of a problem and the goals. The new planner, called MOGRT, uses a weighted A* strategy and a multiobjective heuristic function, which is computed over a weighted hierarchy of user-defined criteria. Its computation is based on sets of non-dominated value-vectors assigned to the problem facts, which estimate the total cost of achieving the facts from the goals, using alternative paths. Experiments show that a change in the weights affects both the quality of the resulting plan and the planning time. The proposed approach can easily be adopted by all modern heuristic state-space planners. Keywords: Planning, Heuristic Search, Multiple Criteria, Multiobjective Search 1.