A different take on the best-first game tree pruning algorithms

TL;DR

This paper compares best-first and alpha-beta pruning algorithms in game tree search, analyzing their complexities, historical context, and recent advancements to clarify their relationships and practical performance.

Contribution

It provides a novel perspective on understanding SSS* and best-first algorithms, bridging the gap between theoretical complexity and practical implementation.

Findings

Best-first approaches can be viewed as enhanced depth-first algorithms.

Growing computational power reduces memory concerns of SSS*.

Experimental results compare the effectiveness of recent improvements.

Abstract

The alpha-beta pruning algorithms have been popular in game tree searching ever since they were discovered. Numerous enhancements are proposed in literature and it is often overwhelming as to which would be the best for implementation. A certain enhancement can take far too long to fine tune its hyper parameters or to decide whether it is going to not make much of a difference due to the memory limitations. On the other hand are the best first pruning techniques, mostly the counterparts of the infamous SSS* algorithm, the algorithm which proved out to be disruptive at the time of its discovery but gradually became outcast as being too memory intensive and having a higher time complexity. Later research doesn't see the best first approaches to be completely different from the depth first based enhancements but both seem to be transitionary in the sense that a best first approach could be looked as a depth first approach with a certain set of enhancements and with the growing power of the computers, SSS* didn't seem to be as taxing on the memory either. Even so, there seems to be quite difficulty in understanding the nature of the SSS* algorithm, why it does what it does and it being termed as being too complex to fathom, visualize and understand on an intellectual level. This article tries to bridge this gap and provide some experimental results comparing the two with the most promising advances.