Incorporating Search Algorithms into RTS Game Agents

Authors

  • David Churchill University of Alberta
  • Michael Buro University of Alberta

DOI:

https://doi.org/10.1609/aiide.v8i3.12548

Keywords:

Heuristic Search, Real-Time Strategy Games, Combat Module

Abstract

Real-time strategy (RTS) games are known to be one of the most complex game
genres for humans to play, as well as one of the most difficult games for
computer AI agents to play well. To tackle the task of applying AI to RTS
games, recent techniques have focused on a divide-and-conquer approach,
splitting the game into strategic components, and developing separate systems
to solve each. This trend gives rise to a new problem: how to tie these
systems together into a functional real-time strategy game playing agent. In
this paper we discuss the architecture of UAlbertaBot, our entry into the 2011/2012 StarCraft AI competitions, and the techniques used to include heuristic search based AI systems for the intelligent automation of both build order planning and unit control for combat scenarios.

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Published

2021-06-30

How to Cite

Churchill, D., & Buro, M. (2021). Incorporating Search Algorithms into RTS Game Agents. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 8(3), 2-7. https://doi.org/10.1609/aiide.v8i3.12548

Issue

Vol. 8 No. 3 (2012): AIIDE Workshop Technical Report WS-12-15 (Artificial Intelligence in Adversarial Real-Time Games)

Section

Artificial Intelligence in Adversarial Real-Time Games