Adaptive Artificial Intelligence in Real-Time Strategy Games

Jason Traish

    Research output: ThesisDoctoral Thesis

    694 Downloads (Pure)

    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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