Action Selection for Hammer Shots in Curling: Optimization of Non-convex Continuous Actions With Stochastic Action Outcomes

• Author / Creator

  Ahmad, Zaheen F

• Optimal decision making in the face of uncertainty is an active area of research in artificial intelligence. In this thesis, I present the sport of curling as a novel application domain for research in optimal decision making. I focus on one aspect of the sport, the hammer shot, the last shot taken before a score is given, and how selecting this shot can be modelled as a low-dimensional optimization problem with a continuous action space and stochastic transitions. I explore the unique research challenges that are brought forth when optimizing in a setting where there is uncertainty in the action outcomes. I then survey several existing optimization strategies and describe a new optimization algorithm called Delaunay Sampling, adapted from a method based on Delaunay triangulation. I compare the performance of Delaunay Sampling with the other algorithms using our curling physics simulator and show that it outperforms these other algorithms. I also show that, with a few caveats, Delaunay Sampling exceeds the performance of Olympic-level humans when selecting strategies for hammer shots.

• Subjects / Keywords

  • Curling
  • Optimization
  • Games
  • Stochastic

• Graduation date

  Spring 2017

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3MS3KD3C

• 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.