
Contributed to the uprm-inso4117-2024-2025-s2/semester-project-briscas repository by developing a modular AI system for the Briscas card game, introducing difficulty tiers and advanced decision-making that analyzes opponent moves and trump suits. Enhanced the codebase using JavaScript and object-oriented programming, refactoring AI logic into maintainable components for easier future tuning. Addressed a critical bug in card play validation, ensuring players can only play cards they hold and improving error handling for invalid moves. The work focused on game development, asynchronous programming, and refactoring, resulting in more realistic AI behavior, improved gameplay integrity, and a smoother user experience.
March 2025 (2025-03) — Delivered Brisca AI enhancements and architecture refactors in uprm-inso4117-2024-2025-s2/semester-project-briscas. Key features include Brisca AI with card playing and difficulty tiers (Easy, Normal, Hard) and advanced decision-making that analyzes opponent moves and trump suits, including strategies to play low-value cards and leverage trump opportunities. Architectural improvements refactor AI logic into modular components: AI split into AIEasy, AINormal, AIHard, with integration via AIPLayerModel. Also introduced a generic hard AI option. These changes lay groundwork for more realistic AI behavior and easier future tuning. Commits demonstrate CI-ready, incremental progress (e.g., makeMove function addition; merge BriscasAI changes into AIPLayerModel; refactor AIPlayerModel.js into three files; add generic hard AI). Business impact centers on delivering a more engaging player experience, reducing technical debt, and enabling scalable AI enhancements.
March 2025 (2025-03) — Delivered Brisca AI enhancements and architecture refactors in uprm-inso4117-2024-2025-s2/semester-project-briscas. Key features include Brisca AI with card playing and difficulty tiers (Easy, Normal, Hard) and advanced decision-making that analyzes opponent moves and trump suits, including strategies to play low-value cards and leverage trump opportunities. Architectural improvements refactor AI logic into modular components: AI split into AIEasy, AINormal, AIHard, with integration via AIPLayerModel. Also introduced a generic hard AI option. These changes lay groundwork for more realistic AI behavior and easier future tuning. Commits demonstrate CI-ready, incremental progress (e.g., makeMove function addition; merge BriscasAI changes into AIPLayerModel; refactor AIPlayerModel.js into three files; add generic hard AI). Business impact centers on delivering a more engaging player experience, reducing technical debt, and enabling scalable AI enhancements.
February 2025 monthly summary for uprm-inso4117-2024-2025-s2/semester-project-briscas: Delivered a critical bug fix enhancing card play validation to ensure players can only play cards they hold, with strengthened error handling. This change improves game integrity, reduces invalid moves, and contributes to a smoother user experience in the Briscas project.
February 2025 monthly summary for uprm-inso4117-2024-2025-s2/semester-project-briscas: Delivered a critical bug fix enhancing card play validation to ensure players can only play cards they hold, with strengthened error handling. This change improves game integrity, reduces invalid moves, and contributes to a smoother user experience in the Briscas project.

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