Computer games are prevalent as entertainment. Machine learning techniques can be used to help the computer player become smarter by learning from its experiences. For example, the computer player can learn which moves to make in different situations.

This project aims to investigate machine learning techniques for computer games.
More specifically, the objectives are:
  • machine learning can be achieved from historical data (experience)
  • machine learning algorithms can be applied to computer games
  • understanding the learning task of recognizing a winning board
  • understanding a decision-tree learning algorithm
  • a better understanding of search and knowledge representation
  • evaluation of machine learning algorithms

Students should understand fundamental computer science concepts in Data Structures and Algorithms (CSE 2010) before taking Introduction to Artificial Intelligence (CSE 4301).

In the AI course the students should have learned the fundamental concepts in search, knowledge representation, and a decision-tree learning algorithm, for example, (Russell & Norvig, 2003, p. 653-660).

The decision-tree learning algorithm in (Russell & Norvig, 2003, p. 653-660) is based on Quinlan's (1986) ID3 algorithm. Given a dataset with each data instance labeled with a class, the algorithm recursively finds an attribute that can "best" split the instances into homogeneous subsets with respect to the class labels. The learned tree can then be used to predict class labels of instances that are not used during the learning process.

The detailed project description is available in the PDF file Machine Learning for Games.pdf. You will need the free Adobe Acrobat Reader to view this file.

Russell, S. & Norvig, P. (2003). Artificial Intelligence: A Modern Approach. Second Edition. Prentice Hall, Upper Saddle River, NJ.

Quinlan, J. R. (1986). Induction of Decision Trees. Machine Learning, 1:81-106.