A More Human Way to Play Computer Chess
Kieran Greer

TL;DR
This paper introduces a human-inspired forward-pruning technique for computer chess using 'Move Tables' that incorporate long and short-term memories, improving search efficiency and reducing blunders, achieving top-level playing strength.
Contribution
It presents a novel move table-based forward-pruning method with automatic feature analysis, enhancing search accuracy and mimicking human-like decision processes in chess algorithms.
Findings
Eliminates obvious blunders from forward pruning
Achieves competitive top-level playing strength
Incorporates automatic key square analysis for guided search
Abstract
This paper suggests a forward-pruning technique for computer chess that uses 'Move Tables', which are like Transposition Tables, but for moves not positions. They use an efficient memory structure and has put the design into the context of long and short-term memories. The long-term memory updates a play path with weight reinforcement, while the short-term memory can be immediately added or removed. With this, 'long branches' can play a short path, before returning to a full search at the resulting leaf nodes. Re-using an earlier search path allows the tree to be forward-pruned, which is known to be dangerous, because it removes part of the search process. Additional checks are therefore made and moves can even be re-added when the search result is unsatisfactory. Automatic feature analysis is now central to the algorithm, where key squares and related squares can be generated…
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Taxonomy
TopicsArtificial Intelligence in Games · Reinforcement Learning in Robotics · Video Analysis and Summarization
