Validation and Implementation of ILBFS

TL;DR

This paper introduces ILBFS, a simpler and more intuitive implementation of RBFS, validating its memory efficiency and node expansion order, thereby promoting its adoption in heuristic search applications.

Contribution

First implementation and validation of ILBFS, demonstrating its effectiveness and facilitating adoption of RBFS-like algorithms in practice.

Findings

ILBFS maintains linear space complexity similar to RBFS.

ILBFS's node expansion order aligns with theoretical expectations.

Implementation details like tie-breaking are critical for ILBFS performance.

Abstract

Recursive Best-First Search (RBFS) is a heuristic search algorithm known for its efficient memory usage compared to traditional best-first search methods like A*. Despite its theoretical advantages, RBFS is complex and difficult to teach and to implement, limiting its widespread adoption. To address these challenges, Iterative Linear Best-First Search (ILBFS) was introduced as a simpler, more intuitive alternative while maintaining the linear space complexity of RBFS. In this paper, we present the first implementation of ILBFS, validate its memory usage and node expansion order claims, and explore critical aspects of its implementation, such as tie-breaking and node deletion mechanisms. Our findings demonstrate that ILBFS can serve as an effective stepping stone for researchers and practitioners looking to use memory efficient best-first search methods, facilitating the adoption of RBFS-like algorithms.