Better Time Constrained Search via Randomization and Postprocessing

Abstract

Most of the satisficing planners which are based on heuristic search iteratively improve their solution quality through an anytime approach. Typically, the lowest-cost solution found so far is used to constrain the search. This avoids areas of the state space which cannot directly lead to lower cost solutions. However, in this paper we show that when used in conjunction with a post-processing plan improvement system such as ARAS, this bounding approach can harm a planner’s performance since the bound may prevent the search from ever finding additional plans for the post-processor to improve.

The new anytime search framework of Diverse Any-Time Search addresses this issue through the use of restarts, randomization, and by not bounding as strictly as is done by previous approaches. Below, we will show that by using these techniques, the framework is able to generate a more diverse set of “raw" input plans for the post-processor to work on. We then show that when adding both Diverse Any-Time Search and the ARAS post-processor to LAMA-2011, the winner of the most recent IPC planning competition, the performance according to the IPC scoring metric improves from 511 points to over 570 points when tested on the 550 problems from IPC 2008 and IPC 2011. Performance gains are also seen when these techniques are added to Anytime Explicit Estimation Algorithm (AEES), as the performance improves from 440 points to over 513 points on the same problem set.