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Learning rules of a card game from video

Learning rules of a card game from video 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence Review Springer Journals

Learning rules of a card game from video

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References (5)

Publisher
Springer Journals
Copyright
Copyright © 2011 by Springer Science+Business Media B.V.
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Computer Science, general
ISSN
0269-2821
eISSN
1573-7462
DOI
10.1007/s10462-011-9255-5
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Artificial Intelligence Review – Springer Journals

Published: May 28, 2011

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