Mohammadreza Hami, Nathan Sturtevant

Suboptimal Search with Dynamic Distribution of Suboptimality (Extended Abstract)

Introduction

Suboptimal search with dynamic distribution of suboptimality (extended abstract). Discover DSWA*, a novel algorithm from AAAI 2025 that dynamically distributes suboptimality in bounded-suboptimal heuristic search, improving efficiency.

Abstract

In bounded-suboptimal heuristic search, the aim is to find a solution path within a given bound as quickly as possible, which is crucial when computational resources are limited. Recent research has demonstrated Weighted A* variants such as XDP that find bounded suboptimal solutions without needing to perform state re-expansions; they work by shifting where the suboptimality in the search is allowed. However, the suboptimality distribution is fixed before the search begins. This abstract describes Dynamic Suboptimality Weighted A* (DSWA*), an algorithm introduced at AAAI 2025 that allows suboptimality to be dynamically distributed at runtime based on the properties of the search.

Review

The proposed algorithm, Dynamic Suboptimality Weighted A* (DSWA*), addresses a critical challenge in bounded-suboptimal heuristic search: the static nature of suboptimality distribution in current state-of-the-art methods. The abstract effectively highlights the established need for finding solutions within a given bound quickly, particularly in resource-constrained environments, and positions DSWA* as an evolution beyond algorithms like XDP. By introducing the concept of dynamically adjusting suboptimality allowance at runtime based on ongoing search properties, DSWA* promises a more adaptive and potentially more efficient approach to resource management in search problems, aiming to overcome the limitations of fixed, pre-defined suboptimality distributions. The core contribution outlined here is a significant conceptual advancement for the field of heuristic search. The ability to dynamically distribute suboptimality suggests a potential for more intelligent and responsive search processes that can adapt to varying problem landscapes and real-time computational demands. This could lead to substantial improvements in solution quality-to-time trade-offs, making bounded-suboptimal search more practical for domains requiring rapid decision-making, such as robotics, game AI, or complex logistical planning. The idea of tailoring suboptimality based on search characteristics intuitively points towards a more optimized use of computational resources, offering a promising avenue for pushing the performance boundaries of suboptimal pathfinding. While the abstract outlines a highly promising direction, the full paper will need to elaborate on several critical aspects. Chief among these is the precise mechanism by which DSWA* dynamically distributes suboptimality: what specific "properties of the search" will it leverage (e.g., node depth, frontier size, local heuristic accuracy estimates), and how will these properties translate into adjustments in the suboptimality allowance? The computational overhead associated with this dynamic process will also be a key consideration, requiring careful analysis to ensure its benefits outweigh any added complexity. Furthermore, a detailed theoretical analysis of DSWA*'s optimality guarantees and completeness, alongside a comprehensive empirical evaluation against existing state-of-the-art bounded-suboptimal search algorithms across diverse problem sets, will be essential to fully demonstrate its practical utility and validate its claims.

Full Text

Comments

You need to be logged in to post a comment.