The FF planning system uses heuristic search to generate plans efficiently. Unlike HSP, FF does not assume facts are independent and uses a novel search strategy combining hill-climbing with systematic search. It also employs pruning techniques to reduce search space. FF outperformed other planners in the AIPS-2000 competition. The system uses relaxed GRAPHPLAN as a heuristic estimator, which ignores delete lists and provides more accurate goal distance estimates. FF's search algorithm uses enforced hill-climbing, which combines local and systematic search. It also includes a pruning technique that selects promising successors and another that cuts out branches where goals are achieved too early. FF's performance is evaluated on various benchmark domains, showing it generates solutions quickly. The system is efficient on many planning tasks, though it may not work well on tasks with complex structures. FF's architecture includes a base system with optimizations for special cases. The system uses a heuristic method based on relaxed planning tasks, which are defined by ignoring delete lists. FF's heuristic estimates are derived from GRAPHPLAN, which is proven to solve relaxed tasks in polynomial time. The system uses a greedy strategy to select actions, and includes techniques like NOOPs-first and difficulty heuristic to optimize solution length. FF's implementation is efficient, exploiting the structure of relaxed planning tasks. The system uses a combination of hill-climbing and systematic search to find solutions, and includes pruning techniques to reduce search space. FF's performance is evaluated on various benchmark domains, showing it generates solutions quickly. The system is efficient on many planning tasks, though it may not work well on tasks with complex structures.The FF planning system uses heuristic search to generate plans efficiently. Unlike HSP, FF does not assume facts are independent and uses a novel search strategy combining hill-climbing with systematic search. It also employs pruning techniques to reduce search space. FF outperformed other planners in the AIPS-2000 competition. The system uses relaxed GRAPHPLAN as a heuristic estimator, which ignores delete lists and provides more accurate goal distance estimates. FF's search algorithm uses enforced hill-climbing, which combines local and systematic search. It also includes a pruning technique that selects promising successors and another that cuts out branches where goals are achieved too early. FF's performance is evaluated on various benchmark domains, showing it generates solutions quickly. The system is efficient on many planning tasks, though it may not work well on tasks with complex structures. FF's architecture includes a base system with optimizations for special cases. The system uses a heuristic method based on relaxed planning tasks, which are defined by ignoring delete lists. FF's heuristic estimates are derived from GRAPHPLAN, which is proven to solve relaxed tasks in polynomial time. The system uses a greedy strategy to select actions, and includes techniques like NOOPs-first and difficulty heuristic to optimize solution length. FF's implementation is efficient, exploiting the structure of relaxed planning tasks. The system uses a combination of hill-climbing and systematic search to find solutions, and includes pruning techniques to reduce search space. FF's performance is evaluated on various benchmark domains, showing it generates solutions quickly. The system is efficient on many planning tasks, though it may not work well on tasks with complex structures.