Handling Soft Constraints and Goals Preferences in SGPlan∗

In this paper, we present the partition-and-resolve strategy in SGPlan (hereafter called SGPLan5) for fully supporting all language features in PDDL3.0. Based on the architecture of SGPlan that supported PDDL2.2 (hereafter called SGPLan4), SGPLan5 partitions a large planning problem into subproblems, each with its own subgoal, and resolves those inconsistent solutions using our extended saddle-point condition. Subgoal partitioning is effective for solving large planning problems because each partitioned subproblem involves a substantially smaller search space than that of the original problem. In SGPLan5, we generalize subgoal partitioning so that the goal state of a subproblem is no longer one goal fact as in SGPLan4, but can be any fact with loosely coupled constraints with other subproblems. We have further developed methods for representing a planning problem in a multi-valued form and for carrying out partitioning in the transformed space. The multi-valued representation leads to more efficient heuristics for resolving trajectory and temporal constraints and goal preferences.