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Massachusetts Institute of Technology

The future of game Al : learning to use human tactics

Abstract

dc:description.abstract

Many video games require artificial players that can act as replacements for human players. With today's constant increases in the complexity of games and the prevalence of user-created content, creating an Al that is sufficiently adaptable is becoming more challenging than ever. This thesis proposes a system which takes data from games played between humans, and from that data learns their most effective tactics. In this way, it may be possible to create an Al whose potential is limited only by the human imagination. The prototype that is described in this paper is made up of four components: a Log Interpreter and an Evaluator that process the data gathered from games played by humans; an Organizer that utilizes machine learning to glean useful information from the processed data; and a decision-making bot that uses this information while playing the game. The prototype is implemented using the Torque Game Engine and is applied to a game called TenXion, developed at the Singapore-MIT GAMBIT Game Lab. The implementation that was used for this project is merely the foundation for an efficient, robust system that can accomplish the goal of learning from human tactics. A large portion of this thesis is dedicated to the description of lessons that were learned over the course of the prototype's development and ideas for future extensions, including a map-processing module and better methods of restricting the bot to feasible strategies.

Degree

thesis:*
Department dc:contributor.department
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2009

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Dowgun, Neil M
Advisor dc:contributor.advisor
  • Philip Tan.

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1721.1/61309
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/61309

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Dowgun, Neil M. The future of game Al : learning to use human tactics. Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/61309