Ai Planning for Transportation Logistics

In the last decade the efficiency of the Artificial Intelligence Planning Systems has been increased significantly. New systems appeared that are able to cope with planning problems being orders of magnitude more complex than the ones solvable in early 90's. This vast improvement increase was made possible mainly by three new approaches in plan generation: planning graphs, satisfiability planning and heuristic state-space planning. The latter approach, which is the most powerful one, derives a heuristic function from the specification of a planning problem, independently of its domain, and uses it for guiding the search through the space of the states. During the last years appeared many heuristic state-space planners, such as ASP, HSP, GRT and FF, which were able to solve large transportation logistics problems, with numerous locations, trucks and objects that have to be transferred, very efficiently, as it has been shown in the recent international planning competitions. This paper briefly presents the current status in domain-independent heuristic state-space planning and concentrates on the GRT and MO-GRT planners, where the latter is a recent extension of GRT being able to consider multiple criteria in the plan generation and evaluation process. Finally, the paper outlines results of running MO-GRT in some transportation logistics problems and poses directions for future research.