Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2009

Abstract

This paper illustrates how we create a software agent by employing FALCON, a self-organizing neural network that performs reinforcement learning, to play a well-known first person shooter computer game known as Unreal Tournament 2004. Through interacting with the game environment and its opponents, our agent learns in real-time without any human intervention. Our agent bot participated in the 2K Bot Prize competition, similar to the Turing test for intelligent agents, wherein human judges were tasked to identify whether their opponents in the game were human players or virtual agents. To perform well in the competition, an agent must act like human and be able to adapt to some changes made to the game. Although our agent did not emerge top in terms of humanlike, the overall performance of our agent was encouraging as it acquired the highest game score while staying convincing to be human-like in some judges’ opinions.

Discipline

Artificial Intelligence and Robotics

Research Areas

Data Science and Engineering

Publication

Proceedings of 21st Innovative Applications of Artificial Intelligence Conference, IAAI-09, Pasadena, California, 2009 July 14-16

First Page

173

Last Page

178

ISBN

9781577354239

Publisher

AAAI

City or Country

Pasadena

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