Multiobjective heuristic state-space planning

Modern domain-independent heuristic planners evaluate their plans on the basis of their length only. However, in real-world problems there are other criteria that also play an important role, such as resource consumption, profit, safety, etc. This paper extends the GRT planner, an efficient domain-independent heuristic state-space planner, with the ability to consider multiple criteria. The heuristic of GRT is based on the estimation of the distances between each fact of a problem and the goals. The new planner, called MOGRT, uses a weighted A* strategy and a multiobjective heuristic function, which is computed over a weighted hierarchy of user-defined criteria. Its computation is based on sets of non-dominated value-vectors assigned to the problem facts, which estimate the total cost of achieving the facts from the goals, using alternative paths. Experiments show that a change in the weights affects both the quality of the resulting plan and the planning time. The proposed approach can easily be adopted by all modern heuristic state-space planners.