
Pedro Matos 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. He refactored the AI architecture into maintainable components, splitting logic into AIEasy, AINormal, and AIHard classes, and integrated them through a central model. Using JavaScript and object-oriented programming, Pedro also addressed a critical bug in card play validation, ensuring players can only play cards they hold. His work improved game integrity, enhanced user experience, and established a scalable foundation for future AI enhancements and refactoring.

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.
Overview of all repositories you've contributed to across your timeline