Exploiting competitive planner performance

Abstract

To date, no one planner has demonstrated clearly superior performance. Although researchers have hypothesized that this should be the case, no one has performed a large study to test its limits. In this research, we tested performance of a set of planners to determine which is best on what types of problems. The study included six plan-ners and over 200 problems. We found that performance, as measured by number of problems solved and computation time, varied with no one planner solving all the problems or being consistently fastest. Analysis of the data also showed that most planners either fail or succeed quickly and that performance depends at least in part on some easily observa-ble problem/domain features. Based on these results, we implemented a meta-planner that interleaves execution of six planners on a problem until one of them solves it. The control strategy for ordering the plan-ners and allocating time is derived from the performance study data. We found that our meta-planner is able to solve more problems than any single planner, but at the expense of computation time.