State Evaluation and Opponent Modelling in Real-Time Strategy Games

• Author / Creator

  Erickson, Graham KS

• Designing competitive Artificial Intelligence (AI) systems for Real-Time Strategy
  (RTS) games often requires a large amount of expert knowledge (resulting in hard-coded
  rules for the AI system to follow). However, aspects of an RTS agent can
  be learned from human replay data. In this thesis, we present two ways in which
  information relevant to AI system design can be learned from replays, using the
  game StarCraft for experimentation. First we examine the problem of constructing
  build-order game payoff matrices from replay data, by clustering build-orders from
  real games. Clusters can be regarded as strategies and the resulting matrix can be
  populated with the results from the replay data. The matrix can be used to both
  examine the balance of a game and find which strategies are effective against which
  other strategies. Next we look at state evaluation and opponent modelling. We
  identify important features for predicting which player will win a given match.
  Model weights are learned from replays using logistic regression. We also present
  a metric for estimating player skill, which can be used as features in the predictive
  model, that is computed using a battle simulation as a baseline to compare player
  performance against. We test our model on human replay data giving a prediction
  accuracy of > 70% in later game states. Additionally, our player skill estimation
  technique is tested using data from a StarCraft AI system tournament, showing
  correlation between skill estimates and tournament standings.

• Subjects / Keywords

  • Real-Time Strategy
  • Machine Learning
  • Artificial Intelligence

• Graduation date

  Fall 2014

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3H978

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.