Developed a robust, data-driven monster and AI framework for the NbcampUnreal/2nd-Team1-Final-Project repository, focusing on scalable content iteration and reliable combat systems. Leveraging C++, Unreal Engine Blueprints, and data tables, the work consolidated monster logic into extensible base classes, streamlined asset and animation integration, and introduced tools for rapid testing and stat tuning. Combat reliability was enhanced through comprehensive collision and hit detection fixes, while UI and audio systems were refined with health bars, damage widgets, and sound cues. The codebase was kept clean through systematic refactoring, legacy code removal, and the addition of developer-focused testing utilities.
June 2025 performance: Delivered data-driven monster design updates, combat reliability improvements, and UI/UX/audio polish across the NbcampUnreal/2nd-Team1-Final-Project. Focused on delivering concrete business value: faster stat tuning via data tables, stable combat experiences, richer boss encounters, and clearer feedback in-game, while maintaining a clean codebase and robust tooling.
June 2025 performance: Delivered data-driven monster design updates, combat reliability improvements, and UI/UX/audio polish across the NbcampUnreal/2nd-Team1-Final-Project. Focused on delivering concrete business value: faster stat tuning via data tables, stable combat experiences, richer boss encounters, and clearer feedback in-game, while maintaining a clean codebase and robust tooling.
May 2025 highlights: Delivered a scalable AI/Monster framework and data-driven monster system, enabling rapid content creation, balance, and QA for dungeon enemies. Key features include AI/Monster scaffolding and asset renaming, dungeon monster assets/animations and test maps, CheatManager integration with test utilities, and consolidation of monster logic into a base class driven by data tables. Introduced desert monsters (Lizardman and Boss Worm) with C++ scaffolding and test spawns, plus ongoing animation/montage improvements and packaging/collision stability fixes. This work provides a robust, data-driven foundation for iterating content with lower risk and faster turnaround times.
May 2025 highlights: Delivered a scalable AI/Monster framework and data-driven monster system, enabling rapid content creation, balance, and QA for dungeon enemies. Key features include AI/Monster scaffolding and asset renaming, dungeon monster assets/animations and test maps, CheatManager integration with test utilities, and consolidation of monster logic into a base class driven by data tables. Introduced desert monsters (Lizardman and Boss Worm) with C++ scaffolding and test spawns, plus ongoing animation/montage improvements and packaging/collision stability fixes. This work provides a robust, data-driven foundation for iterating content with lower risk and faster turnaround times.
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