The SSDF Chess Engine Rating list, 2019-12

This is the Chess Engine Rating list of 2019-12 from Svenska schackdatorföreningen, the Swedish Chess Computer Association. SSDF has produced the list for 22 years. Here we supply the 'Selected 50' subset with headline notes on each engine. SSDF's 2019-12 narrative, top 50 and long lists (both with match details) may also be downloaded from here

The SSDF Chess Engine Rating List, 2019-02

This is the Chess Engine Rating list of 2019-02 from Svenska schackdatorföreningen, the Swedish Chess Computer Association. SSDF has produced the list for 21 years. Here we supply the 'Selected 50' subset with headline notes on each engine. SSDF's 2019-02 narrative and long list may also be downloaded from here

The SSDF chess engine rating list, 2018-10

This is the Chess Engine Rating list of 2018-10 from Svenska schackdatorföreningen, the Swedish Chess Computer Association. SSDF has produced the list for 21 years. Here we supply the 'Selected 50' subset with headline notes on each engine. The full details of the 2018-10 may also be downloaded from here

The SSDF rating list, 2020-07, in memoriam Tony Hedlund

This is the Chess Engine Rating list of 2020-07 from Svenska schackdatorföreningen, the Swedish Chess Computer Association. It is dedicated to the late Tony Hedlund, a leading contributor to SSDF who also managed its database of games. SSDF has produced the list for 23 years. Here we supply the 'Selected 50' subset with headline notes on each engine. SSDF's 2020-07 narrative, top 50 and long lists (both with match details) may also be downloaded from here

Skill Rating by Bayesian Inference

Systems Engineering often involves computer modelling the behaviour of proposed systems and their components. Where a component is human, fallibility must be modelled by a stochastic agent. The identification of a model of decision-making over quantifiable options is investigated using the game-domain of Chess. Bayesian methods are used to infer the distribution of players’ skill levels from the moves they play rather than from their competitive results. The approach is used on large sets of games by players across a broad FIDE Elo range, and is in principle applicable to any scenario where high-value decisions are being made under pressure

Skilloscopy: Bayesian modeling of decision makers' skill

This paper proposes and demonstrates an approach, Skilloscopy, to the assessment of decision makers. In an increasingly sophisticated, connected and information-rich world, decision making is becoming both more important and more difficult. At the same time, modelling decision-making on computers is becoming more feasible and of interest, partly because the information-input to those decisions is increasingly on record. The aims of Skilloscopy are to rate and rank decision makers in a domain relative to each other: the aims do not include an analysis of why a decision is wrong or suboptimal, nor the modelling of the underlying cognitive process of making the decisions. In the proposed method a decision-maker is characterised by a probability distribution of their competence in choosing among quantifiable alternatives. This probability distribution is derived by classic Bayesian inference from a combination of prior belief and the evidence of the decisions. Thus, decision-makers’ skills may be better compared, rated and ranked. The proposed method is applied and evaluated in the gamedomain of Chess. A large set of games by players across a broad range of the World Chess Federation (FIDE) Elo ratings has been used to infer the distribution of players’ rating directly from the moves they play rather than from game outcomes. Demonstration applications address questions frequently asked by the Chess community regarding the stability of the Elo rating scale, the comparison of players of different eras and/or leagues, and controversial incidents possibly involving fraud. The method of Skilloscopy may be applied in any decision domain where the value of the decision-options can be quantified

Gender, competition and performance: Evidence from real tournaments

There is a growing literature looking at how men and women respond differently to competition. We contribute to this literature by studying gender differences in performance in a high-stakes and male dominated competitive environment, expert chess tournaments. Our findings show that women underperform compared to men of the same ability and that the gender composition of games drives this effect. Using within player variation in the conditionally random gender of their opponent, we find that women earn significantly worse outcomes against male opponents. We examine the mechanisms through which this effect operates by using a unique measure of within game quality of play. We find that the gender composition effect is driven by women playing worse against men, rather than by men playing better against women. The gender of the opponent does not affect a male player’s quality of play. We also find that men persist longer against women before resigning. These results suggest that the gender composition of competitions affects the behavior of both men and women in ways that are detrimental to the performance of women. Lastly, we study the effect of competitive pressure and find that players’ quality of play deteriorates when stakes increase, though we find no differential effect over the gender composition of games

Building a computer poker agent with emphasis on opponent modeling

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (pages 53-54).In this thesis, we present a computer agent for the game of no-limit Texas Hold'em Poker for two players. Poker is a partially observable, stochastic, multi-agent, sequential game. This combination of characteristics makes it a very challenging game to master for both human and computer players. We explore this problem from an opponent modeling perspective, using data mining to build a database of player styles that allows our agent to quickly model the strategy of any new opponent. The opponent model is then used to develop a robust counter strategy. A simpler version of this agent modified for a three player game was able to win the 2011 MIT Poker Bot Competition.by Jian Huang.M. Eng

Educational Chess Program

Práce se zabývá tvorbou umělé inteligence a uživatelského rozhraní pro šachový program. V textu je také popsán návrh a implementace výukové části pro tuto aplikaci. Jsou zde analyzovány šachové motory a současné šachové programy zaměřující se na výuku.This bachelor thesis deals problems with an artificial intelligence and a user interface of a chess program. There is also a description of design and implementation of a tutorial part for this program. Chess engines and current chess programs focused on teaching are being analysed, too.

On forward pruning in game-tree search

Ph.DDOCTOR OF PHILOSOPH