Learning rules of a card game from video

摘要

This paper presents a framework for automatically learning rules of a simple game of cards using data from a vision system observing the game being played. Incremental learning of object and protocol models from video, for use by an artificial cognitive agent, is presented. iLearn—a novel algorithm for inducing univariate decision trees for symbolic datasets is introduced. iLearn builds the decision tree in an incremental way allowing automatic learning of rules of the game.