Applying and Improving Monte-Carlo Tree Search in a Fighting Game AI-Reference-Cited by-同舟云学术

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Applying and Improving Monte-Carlo Tree Search in a Fighting Game AI

Published:2016-11-09 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 13th International Conference on Advances in Computer Entertainment Technology
language:
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Author:

Ishihara Makoto¹,Miyazaki Taichi²,Chu Chun Yin³,Harada Tomohiro²,Thawonmas Ruck²

Affiliation:

1. Graduate School of information Science and Engineering, Ritsumeikan University, Shiga, Japan

2. College of Information Science and Engineering, Ritsumeikan University, Shiga, Japan

3. Graduate School of information Science and Engineering, Ritsumeican University, Shiga, Japan

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3001773.3001797

Reference7 articles.

1. Knowledge-based fast evolutionary MCTS for general video game playing

2. The grand challenge of computer Go

3. Monte Carlo Tree Search for Collaboration Control of Ghosts in Ms. Pac-Man

4. Bandit Based Monte-Carlo Planning

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1. Surrogate-assisted Monte Carlo Tree Search for real-time video games;Engineering Applications of Artificial Intelligence;2024-07

2. Evolving population method for real-time reinforcement learning;Expert Systems with Applications;2023-11

3. Achieving Fairness in DareFightingICE Agents Evaluation Through a Delay Mechanism;2023 IEEE Conference on Games (CoG);2023-08-21

4. Fighting Game Adaptive Background Music for Improved Gameplay;2023 IEEE Conference on Games (CoG);2023-08-21

5. Improving Data Transfer Efficiency for AIs in the DareFightingICE using gRPC;2023 8th International Conference on Business and Industrial Research (ICBIR);2023-05-18

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