Dawson Tomasz, Richard Valenzano

Augmenting Exploration with Locally Greedy Probes

Introduction

Augmenting exploration with locally greedy probes. Improve Greedy Best First Search (GBFS) with locally greedy probes. This method enhances exploration by exploiting 'easy' states, boosting search performance in classical planning domains.

Abstract

Enhancing Greedy Best First Search (GBFS) with stochastic exploration will often greatly improve search performance. In this work, we show that one way exploration does so is by helping the search find states that are "easy" for standard GBFS without exploration. In particular, we show that in problems in which standard GBFS struggles and exploration helps, there are often many states that are reachable from the initial state that standard GBFS can quickly find solutions from. Many such states are actually outside the Bench Transition System (BTS) --- which is a structure that contains all states that standard GBFS may encounter --- meaning GBFS cannot reach them without using exploration. To allow exploration mechanisms to better exploit the existence of such states, we introduce a method called locally greedy probes. Upon a successor having an improved heuristic from its parent, locally greedy probes pause exploration and greedily hill-climb along a single path as long as heuristic improvements keep occurring. Our empirical evaluation shows that this approach is effective at enhancing several exploration mechanisms in a variety of classical planning domains.

Review

This paper presents a compelling analysis of how stochastic exploration enhances Greedy Best First Search (GBFS), identifying that exploration often aids in discovering "easy" states from which GBFS can quickly find solutions. Crucially, many of these beneficial states are found to lie outside the traditional Bench Transition System (BTS), meaning they are inaccessible to standard GBFS without exploratory mechanisms. To leverage this insight, the authors introduce "locally greedy probes," a novel method that, upon detecting a heuristic improvement in a successor state, temporarily pauses exploration to greedily hill-climb along that path as long as further heuristic improvements occur. The core contribution of this work lies in its insightful explanation of *why* exploration is effective, providing a theoretical underpinning for its practical benefits. By pinpointing the role of "easy" states, often residing beyond GBFS's native reach, the paper offers a fresh perspective on heuristic search design. The proposed "locally greedy probes" method is a practical and intuitive approach to capitalize on this understanding, offering a generalizable strategy that can reportedly enhance various existing exploration mechanisms. The abstract indicates a strong empirical validation, with the approach demonstrating effectiveness across a diverse set of classical planning domains, suggesting robustness and significant practical utility. While the abstract provides a strong overview, a more detailed understanding of the precise integration of locally greedy probes with existing exploration mechanisms would be beneficial for a complete review. For instance, clarity on the computational overhead introduced by these probes, the conditions under which exploration is resumed after a probe, and a more rigorous definition of what constitutes an "easy" state would strengthen the theoretical contribution. Nevertheless, this work offers a valuable advancement in the field of heuristic search, providing both a deeper theoretical understanding of exploration's efficacy and a practical, empirically validated method for improving GBFS performance. Future work could explore the topological characteristics of these "easy" states and the potential for dynamically adjusting the aggressiveness of probes.

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