Adaptive Artificial Intelligence in Real-Time Strategy Games

Jason Traish

Research output: ThesisDoctoral Thesis

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Abstract

Highly capable Artificial Intelligences (AI) have been created for board games
such as Go and Chess. Players of these games can play against a computerised
opponent at the equivalent skill level of grandmaster or better. However, such
highly capable AI agents have not yet been developed for Real-Time Strategy
(RTS) games.
RTS agents must address several challenging issues to demonstrate real player
like skill. The first major issue is that RTS games play out in ’real-time’. In the
RTS game context, ’real-time’ means that games do not have a rigid turn-based
structure but play continuously with players taking actions at any time. The sec-
ond major issue is that RTS games have a much larger game state-space than Go
or Chess. This is because typical RTS games occur on large maps with terrain
differentiations, and involve a large number of diverse units. They also involve
many more actions such as resource collection, production management and dif-
ferent types of tactical actions. Finally, unlike Go or Chess, players in RTS games
possess only incomplete game-state information. RTS AI is one of the next big AI
challenges.
This thesis seeks to improve the quality of adaptive tactical RTS agents, by en-
abling them to respond more effectively to novel player actions. This is intended
to improve both the challenge for skilled players and the value of the single-
player experience.
The main focus of the thesis is on the issue of real-time decision making and the
ability to adapt to changes within a game. The thesis provides a framework that
allows an RTS AI to adapt to unknown scenarios without perceptible lag.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Charles Sturt University
Supervisors/Advisors
  • Tulip, James, Principal Supervisor
  • Moore, Wayne, Principal Supervisor
  • Bossomaier, Terry, Principal Supervisor
Award date07 Nov 2017
Publication statusPublished - 2017

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