PLANNING

The work on heuristic planning concerns currently STRIPS worlds and simple problems. The major outcome is GRT, a heuristic state-space planner that constructs its heuristic function in a domain-independent way. The planner achieves significant performance in many domains as it has been shown in the last international planning competition. An extension to the basic planning system uses XOR-constraints, in order to analyze a planning problem in a sequence of easier sub-problems that have to be solved sequentially. XOR-constraints are relations between sets of ground facts, where exactly one of them can hold in each state.

The main research in parallel planning has been focused on ways to parallelize the planning process of any strips planner without altering the resulting plans. We have developed a method called ODMP (Operator Distribution Method for parallel Planning) based on the distribution of semi-grounded operators to the available processors, which manages to speedup any heuristic strips planner while preserving the quality of the resulting plans. The method has been tested on GRT and CL, a domain-dependent strips planner for logistics problems, with remarkable success.

We are working in constructing heuristics for planning problems that take into account multiple criteria, such as plan length, duration, cost, profit, safety etc. The main outcome is MO-GRT, an extension of the GRT planner, with the ability to construct a multi-objective heuristic function in a domain independent way. Criteria, their scales and preferences among them are provided by the user. The planner produces plans of different quality and in different planning times, depending on the defined preferences.

We are working on the exploitation of constraint solvers in planning problems, where resources, criteria, and schedule constraints exist.