AAAI Publications, Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence

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Human and Computer Preferences at Chess
Kenneth Wingate Regan, Tamal Biswas, Jason Zhou

Last modified: 2014-06-18


Distributional analysis of large data-sets of chess games played by humans and those played by computers shows the following differences in preferences and performance:


(1) The average error per move scales uniformly higher the more advantage is enjoyed by either side, with the effect much sharper for humans than computers;


(2) For almost any degree of advantage or disadvantage, a human player has a significant 2--3\% lower scoring expectation if it is his/her turn to move, than when the opponent is to move; the effect is nearly absent for computers.


(3) Humans prefer to drive games into positions with fewer reasonable options and earlier resolutions, even when playing as human-computer {\em freestyle\/} tandems.


The question of whether the phenomenon (1) owes more to human perception of relative value, akin to phenomena documented by Kahneman and Tversky, or to rational risk-taking in unbalanced situations, is also addressed.

Other regularities of human and computer performances are described with implications for decision-agent domains outside chess.


Game playing; Computer chess; Decision making; Statistics; Distributional performance analysis; Human-computer distinguishers

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