Shahaf Shperberg, Natalie Morad, Lior Siag, Ariel Felner, Dor Atzmon

Bidirectional Bounded-Suboptimal Heuristic Search with Consistent Heuristics (Extended Abstract)

Introduction

Bidirectional bounded-suboptimal heuristic search with consistent heuristics (extended abstract). Discover advancements in bidirectional bounded-suboptimal heuristic search. We introduce BAE* variants for consistent heuristics, experimentally comparing their performance against other algorithms.

Abstract

Recent advancements in bidirectional heuristic search have yielded significant theoretical insights and novel algorithms. While most previous work has concentrated on optimal search methods, this paper focuses on bounded-suboptimal bidirectional search, where a bound on the suboptimality of the solution cost is specified. We build upon the state-of-the-art optimal bidirectional search algorithm, BAE*, designed for consistent heuristics, and introduce several variants of BAE* specifically tailored for the bounded-suboptimal context. Through experimental evaluation, we compare the performance of these new variants against other bounded-suboptimal bidirectional algorithms as well as the standard weighted A* algorithm. Our results demonstrate that each algorithm excels under distinct conditions, highlighting the strengths and weaknesses of each approach.

Review

The paper, "Bidirectional Bounded-Suboptimal Heuristic Search with Consistent Heuristics (Extended Abstract)," addresses a critical and often underserved area within heuristic search. While much theoretical and algorithmic development has historically focused on optimal search, the authors pivot to bounded-suboptimal methods, where a user-defined bound on solution suboptimality is acceptable in exchange for potentially faster search. This shift is highly relevant for practical applications where finding a good-enough solution quickly is paramount. The core contribution lies in extending the state-of-the-art optimal bidirectional search algorithm, BAE*, specifically for the bounded-suboptimal context, particularly when consistent heuristics are available. The methodology involves developing several novel variants of BAE* designed to operate under suboptimality bounds. This approach leverages the known strengths of BAE* in optimal bidirectional search with consistent heuristics and adapts them to the more relaxed requirements of bounded-suboptimality. The authors then conduct an experimental evaluation, a crucial step to validate their theoretical developments. They compare these new BAE* variants against existing bounded-suboptimal bidirectional algorithms, as well as the widely used single-agent weighted A* algorithm. This comparative analysis is essential for understanding the practical utility and performance characteristics of the proposed methods. The experimental results indicate that no single algorithm uniformly dominates across all scenarios; instead, each approach "excels under distinct conditions." This finding is valuable, as it not only highlights the specific strengths and weaknesses of each algorithm but also offers guidance for practitioners in selecting the most appropriate method for a given problem instance and desired suboptimality bound. As an "Extended Abstract," this work provides a promising overview and initial results, suggesting a significant step forward in making bidirectional heuristic search more adaptable and efficient for practical, bounded-suboptimal problems.

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