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Combat Models for RTS Games
Journal article   Open access   Peer reviewed

Combat Models for RTS Games

Alberto Uriarte and Santiago Ontanon
IEEE transactions on games, v 10(1), pp 29-41
Mar 2018
DOI: https://doi.org/10.1109/TCIAIG.2017.2669895
url
https://arxiv.org/pdf/1605.05305View
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Abstract

Combat model Computational modeling Context forward model game artificial intelligence (AI) game replay game state abstraction Games learning Monte Carlo methods Monte Carlo tree search (MCTS) Predictive models real-time strategy (RTS) Real-time systems S<sc xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">tar C<sc xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">raft
Game-tree search algorithms, such as Monte Carlo tree search, require access to a forward model (or "simulator") of the game at hand. However, in some games such forward model is not readily available. This paper presents three forward models for two-player attrition games, which we call "combat models," and show how they can be used to simulate combat in real-time strategy games. We also show how these combat models can be learned from replay data. We use S tar C raft as our application domain. We report experiments comparing our combat models predicting combat results and their impact when used for tactical decisions during a real game.

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12 citations in Web of Science
14 citations in Scopus

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Web of Science research areas
Computer Science, Artificial Intelligence
Computer Science, Software Engineering
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