Heuristic Search for Planning with Different Forced Goal-Ordering Constraints

Planning with forced goal-ordering (FGO) constraints has been proposed many times over the years, but there are still major difficulties in realizing these FGOs in plan generation. In certain planning domains, all the FGOs exist in the initial state. No matter which approach is adopted to achieve a subgoal, all the subgoals should be achieved in a given sequence from the initial state. Otherwise, the planning may arrive at a deadlock. For some other planning domains, there is no FGO in the initial state. However, FGO may occur during the planning process if certain subgoal is achieved by an inappropriate approach. This paper contributes to illustrate that it is the excludable constraints among the goal achievement operations (GAO) of different subgoals that introduce the FGOs into the planning problem, and planning with FGO is still a challenge for the heuristic search based planners. Then, a novel multistep forward search algorithm is proposed which can solve the planning problem with different FGOs efficiently

Subgoal ordering and granularity control for incremental planning

In this paper, we study strategies in incremental planning for ordering and grouping subproblems partitioned by the subgoals of a planning problem. To generate a rich set of partial orders for ordering subproblems, we propose an algorithm based on a relaxed plan that ignores the delete lists. The new algorithm considers both the initial and the goal states and can effectively order subgoals in such a way that greatly reduces the number of invalidations during incremental planning. We have also considered trade-offs between the granularity of the subgoal sets and the complexity of solving the overall planning problem. We propose an efficient strategy for dynamically adjusting the grain size in partitioning in order to minimize the total complexity. We further evaluate a redundantordering scheme that uses two different subgoal orders to improve the solution quality, without greatly sacrificing run-time efficiency. Experimental results on using Metric-FF, YAHSP, and LPG-TD-speed as the embedded planners in incremental planning sho