A Forward Search Planning Algorithm with a Goal Ordering Heuristic

Forward chaining is a popular strategy for solving classical planning problems and a number of recent successful planners exploit it. To succeed, a forward chaining algorithm must carefully select its next action. In this paper, we introduce a forward chaining algorithm that selects its next action using heuristics that combine backward regression and goal ordering techniques. Backward regression helps the algorithm focus on actions that are relevant to the achievement of the goal. Goal ordering techniques strengthens this filtering property, forcing the forward search process to consider actions that are relevant at the current stage of the search process. One of the key features of our planner is its dynamic application of goal ordering techniques: we apply them on the main goal as well as on all the derived sub-goals. We compare the performance of our planner with FF – the winner of the AIPS’00 planning competition – on a number of well-known and novel domains. We show that our planner is competitive with FF, outperforming it on more complex domains in which sub-goals are typically non-trivial. List of keywords: forward chaining, backward regression, goal ordering, relaxed problem