Analyzing Positional Play in Chess using Machine Learning
Chess has two broad approaches to game-play, tactical and positional. Tactical play is the approach of calculating maneuvers and employing tactics that take advantage of short-term opportunities, while positional play is dominated by long-term maneuvers for advantage and requires judgement more than calculations. Current generation chess engines predominantly employ tactical play and thus outplay top human players given their much superior computational abilities. Engines do so by searching game trees of depths typically between 20 and 30 moves and calculating a large number of variations. However, human play is often a combination of both, tactical and positional approaches, since humans have some intuition about which board positions are intrinsically better than others. In our project, we use machine learning to identify elements of positional play that can be incorporated in chess engines. We model chess board positions as networks of interacting pieces and predict game outcomes when the engine evaluates both sides to be of comparable strength. Our findings indicate that we can make such predictions with reasonable accuracy and thus, this technique can be augmented with current chess engines to improve their performance.
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