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Amê: an environment to learn and analyze adversarial search algorithms using stochastic card games

Published: 13 April 2015 Publication History

Abstract

Computer Science students are usually enthusiastic about learning Artificial Intelligence (AI) due to the possibility of developing computer games that incorporate AI behaviors. Under this scenario, Search Algorithms (SA) are a fundamental subject of AI for a broad variety of games. Implementing deterministic games, varying from tic-tac-toe to chess games, are commonly approaches used to teach AI. Considering the perspective of game playing, however, stochastic games are usually more fun to play, and are not much explored during AI learning process. Other approaches in AI learning include developing searching algorithms to compete against each other. These approaches are relevant and engaging, but they lack an environment that features both algorithm design and benchmarking capabilities. To address this issue, we present Amê -- an environment to support the learning process and analysis of adversarial search algorithms using a stochastic card game. We have conducted a pilot experiment with Computer Science students that developed different adversarial search algorithms for Hanafuda (a traditional Japanese card game).

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Cited By

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  • (2023) Learning to Play Koi-Koi Hanafuda Card Games With Transformers IEEE Transactions on Artificial Intelligence10.1109/TAI.2023.32406744:6(1449-1460)Online publication date: Dec-2023

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cover image ACM Conferences
SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing
April 2015
2418 pages
ISBN:9781450331968
DOI:10.1145/2695664
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 13 April 2015

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Author Tags

  1. adversarial search
  2. benchmark
  3. learning
  4. search algorithms
  5. stochastic games

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SAC 2015
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SAC 2015: Symposium on Applied Computing
April 13 - 17, 2015
Salamanca, Spain

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SAC '15 Paper Acceptance Rate 291 of 1,211 submissions, 24%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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  • (2023) Learning to Play Koi-Koi Hanafuda Card Games With Transformers IEEE Transactions on Artificial Intelligence10.1109/TAI.2023.32406744:6(1449-1460)Online publication date: Dec-2023

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