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Player Prediction


Pattern Recognition with Sequential Prediction

Fri Mommersteeg (Eindhoven University of Technology, Netherlands)
AI Game Programming Wisdom, 2002.
Topics: Learning, Pattern Matching; Genres: General
Abstract: This article provides a simple but efficient algorithm for recognizing repetitive patterns in number sequences. Pattern recognition is something that humans are very good at, but for a computer this is not so easy. Too often a game AI can be beaten by repeatedly performing the same trick, just because it is unable to perceive the pattern. This article explains how to deal with this problem and shows you how to map game events onto useful number sequences. Furthermore, it describes a few possible applications of the algorithm in computer games.

Using N-Gram Statistical Models to Predict Player Behavior
François Dominic Laramée
AI Game Programming Wisdom, 2002.
Topics: Learning, Pattern Matching; Genres: General
Abstract: N-Grams are statistical constructs used to predict sequences of events in situations that exhibit the property of local structure. Language is one such context: the probability of hearing the word "fries" is higher if one has just heard the word "french" than if one has just heard the word "fruit". Some games, specifically fighting games in which players develop signature move combinations, also exhibit this property. The article describes how to train an AI to recognize patterns and predict the human player's next move using N-Gram models.

 
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