1 / 17

Game-Theoretic Path Selection in Motion-Based Games: Strategies and Experiments

This study explores path selection in motion-based games using game-theoretic approaches. The research investigates various strategies such as Regret Minimization (RM), Horizon-Regret Minimization (HorizonRM), Max-Min (MM), and their derivatives. The framework is validated through experiments in Bomberman, where players navigate, drop bombs, and aim to survive. The findings include performance comparisons in both 1-on-1 and Free-for-All scenarios, assessing the efficacy of traditional and new strategies. The results provide insights into strategic decision-making in dynamic game environments.

tambre
Télécharger la présentation

Game-Theoretic Path Selection in Motion-Based Games: Strategies and Experiments

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Game-Theoretic Selection of Paths in Motion-based Games Joseph Jaeger Dr. Kostas Bekris Andrew Kimmel 1 August 2013

  2. Game Theory Game Theory Max-Min (Shoham & Leyton-Brown 2009) Regret Minimization (Halpern & Pass 2012)

  3. Motion-based Games Motion-based Games Motion Planning

  4. Proposed Framework

  5. Benchmark Game: Bomberman Actions: Move around and drop bombs to destroy the environment/other agents Goal: Be the last agent not destroyed by a bomb

  6. Variation Random Goals Periodic Bomb Placement Continuous Environments

  7. Example Video

  8. Strategies & Experiments Stategies Regret-Min (RM) Horizon-Regret-Min (HorizonRM) Max-Min (MM) Horizon-Max-Min (HorizonMM) Greedy Greedy-Avoidance (GA) Experiments 1-on-1 Free-for-All (4 Player and 8 Player)

  9. 1-on-1 Results

  10. Free-for-All Results

  11. Free-for-All Results

  12. 1-on-1 Results

  13. Free-for-All Results

  14. NewExperiments New Strategies RM+ HorizonRM+ MM+ HorizonMM+ Prior Strategies RM HorizonRM MM HorizonMM Greedy GA

  15. 1-on-1 Results

  16. 1-on-1 Results

  17. Questions?

More Related