Projects:2017s1-121 Learning Procedural Knowledge using Random Forests

Abstract

This project aims to produce artificially intelligent(AI) programs, or agents, that are automatically generated using a learning process. The agents are written in a production rule language and are created by inferring decision trees. A decision tree describes a series of conditions that link observed variables to a conclusion. Decision trees can be generated automatically (or inferred) from training data and random forests of inferred trees have often proven to be very effective classifiers. Although it is less common, decision trees can also be used for AI control of agents. In effect, the agent's behaviour is learned by the system rather than programmed in to it. As outcome of the project, it was possible to produce AI agents by using random forest for simple games such as Tic-Tac-toe and Connect 4. However, the behaviour of the AI agents depend strongly on the quality of training data and gathering these data is a time-consuming process. Also, the AI agent was only able to make decisions based on current environment state and can not foresee the future outcome. Hence, it was concluded that using random forest alone is not an optimal way of developing more complex AI agents. As a solution, it was suggested that a further research needs to be carried out to overcome the limitations of random forest by combining it with reinforcement learning.

Introduction

Project Team

Students: Woong-Ji Choi and Yiming Shitao Supervisor: Dr Braden Phillips and Xin Yuan

Objectives

• Gain knowledge in machine learning method using decision trees and random forest
• Develop AI agent using random forest for game of Tic-Tac-Toe and Connect 4
• Improve the performance of the AI agents to expert level