Proceedings Abstracts of the Twenty-Fourth International Joint Conference on Artificial Intelligence

Deordering and Numeric Macro Actions for Plan Repair / 1673
Enrico Scala, Pietro Torasso

The paper faces the problem of plan repair in presence of numeric information, by providing a new method for the intelligent selection of numeric macro actions. The method relies on a generalization of deordering, extended with new conditions accounting for dependencies and threats implied by the numeric components. The deordering is used as a means to infer (hopefully) minimal ordering constraints then used to extract independent and informative macro actions. Each macro aims at compactly representing a sub-solution for the overall planning problem. To verify the feasibility of the approach, the paper reports experiments in various domains from the International Planning Competition% measuring the performance of the new strategy using two state of the art numeric planning systems; i.e., Colin Metric-FF. Results show (i) the competitiveness of the strategy in terms of coverage, time and quality of the resulting plans wrt current approaches, and (ii) the actual independence from the planner employed.