Large-scale Analysis of Chess Games with Chess Engines: A Preliminary Report

Mathieu Acher 1 François Esnault 1
1 DiverSe - Diversity-centric Software Engineering
IRISA-D4 - LANGAGE ET GÉNIE LOGICIEL, Inria Rennes – Bretagne Atlantique
Abstract : The strength of chess engines together with the availability of numerous chess games have attracted the attention of chess players, data scientists, and researchers during the last decades. State-of-the-art engines now provide an authoritative judgement that can be used in many applications like cheating detection, intrinsic ratings computation, skill assessment, or the study of human decision-making. A key issue for the research community is to gather a large dataset of chess games together with the judgement of chess engines. Unfortunately the analysis of each move takes lots of times. In this paper, we report our effort to analyse almost 5 millions chess games with a computing grid. During summer 2015, we processed 270 millions unique played positions using the Stockfish engine with a quite high depth (20). We populated a database of 1+ tera-octets of chess evaluations, representing an estimated time of 50 years of computation on a single machine. Our effort is a first step towards the replication of research results, the supply of open data and procedures for exploring new directions, and the investigation of software engineering/scalability issues when computing billions of moves.
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  • HAL Id : hal-01307091, version 1
  • ARXIV : 1607.04186

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Mathieu Acher, François Esnault. Large-scale Analysis of Chess Games with Chess Engines: A Preliminary Report. [Technical Report] RT-0479, Inria Rennes Bretagne Atlantique. 2016. ⟨hal-01307091⟩

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