Lior Siag, Ariel Felner, Shahaf Shperberg

Heuristics for Bounded-Suboptimal Search

Introduction

Heuristics for bounded-suboptimal search. Discover a new heuristic method for bounded-suboptimal search (BSS). Combining OHS & USS heuristics, it outperforms standard approaches for BSS algorithms within suboptimality bounds.

Abstract

In heuristic search, it is well-established that different types of heuristics are suited for optimal heuristic search (OHS) and unbounded suboptimal search (USS). In OHS, the heuristic should minimize the error in estimating the true cost of the shortest path, whereas in USS, it is more beneficial for the heuristic to exhibit a clear gradient toward the goal, regardless of the error. However, no study has specifically investigated which heuristic is most effective for bounded suboptimal search (BSS), and the current standard is to use heuristics designed for OHS. This paper introduces a novel method for creating heuristics tailored to BSS by linearly combining heuristics that were designed for OHS and USS. Through experimental evaluation, the proposed method is compared with those suited for OHS and USS. The results demonstrate that, within certain suboptimality bounds, our new heuristic approach outperforms OHS and USS heuristics for various BSS algorithms.

Review

This paper addresses a crucial gap in the field of heuristic search, specifically concerning bounded-suboptimal search (BSS). The authors rightly highlight the well-established distinction between heuristics optimized for optimal heuristic search (OHS) and those for unbounded suboptimal search (USS), noting that BSS currently relies on heuristics primarily designed for OHS. The core contribution is the introduction of a novel method for constructing BSS-tailored heuristics by linearly combining existing OHS and USS heuristics. This approach is highly intuitive, recognizing that BSS lies at an intersection where both path cost accuracy (from OHS) and goal gradient (from USS) might contribute beneficially. The proposed methodology represents a sensible and practical advancement. By leveraging the distinct strengths of OHS heuristics (minimizing error) and USS heuristics (providing a clear goal gradient), the linear combination aims to achieve a balance that is specifically beneficial for BSS. This avoids the limitations of solely adopting an OHS heuristic, which might be overly conservative for a bounded context, or a USS heuristic, which could violate the suboptimality bound. The paper substantiates its claims through experimental evaluation, comparing the performance of the new heuristic against those designed for OHS and USS across various BSS algorithms. The findings, indicating that the novel heuristic approach outperforms OHS and USS heuristics within certain suboptimality bounds, suggest a significant step forward for the practical application of BSS. This work has the potential to redefine how heuristics are designed and selected for bounded-suboptimal problems, offering a more efficient and effective strategy than the current standard. Such an improvement could have broad implications for areas requiring timely, high-quality solutions, where strict optimality is not paramount but guarantees on solution quality are essential. Further research exploring the precise nature of these "certain suboptimality bounds" and how the linear combination parameters correlate with them would be a valuable next step, building upon this promising foundation.

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