Final assignment for a university team project. We coded three different AI based players for a Scopone game. More information about Scopone at: https://it.wikipedia.org/wiki/Scopone (available only in Italian).
- Name of the unit: Artificial Intelligence and Data Analytics (AIDA).
- EM1402, Data Analytics for Business and Society (DABS), Ca' Foscari University of Venice.
- Greedy
- Intermediate
- Monte Carlo Tree Search (MCTS).
- Optimal Decision Making: Our MCTS algorithm combines Reinforcement Learning with tree search to make optimal decisions in card games, even in complex and uncertain environments.
- Adaptive Strategy: It adapts and learns from gameplay experience, continually improving its card-playing strategy by simulating and evaluating potential future moves.
- Competitive Advantage: With this MCTS-based approach, the player can gain a competitive edge in card games by leveraging AI-powered decision-making to outmaneuver its opponents.