Students: Ana Beatriz Cruz, Sabrina Seriques, and Leonardo Preuss
Advisor: Eduardo Ogasawara
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 AI subject for various games. Implementing deterministic games, varying from tic-tac-toe to chess games, are common 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 the AI learning process. Other approaches in AI learning include developing search algorithms to compete against each other. These approaches are relevant and engaging but 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).
Data Privacy
While playing and using AME, no user data is sent or stored at AME. Only scores associated with your gameplay are sent so that you can access your ranking data.