인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 국제인공지능학회 The International Journal of Internet, Broadcasting and Communication The International Journal of Internet, Broadcasting and Communication Vol.17 No.4
- 발행연도
- 2025.11
- 수록면
- 30 - 41 (12page)
이용수
초록· 키워드
We focused on evaluating and comparing two NPC escape strategies—NavMesh, a traditional pathfinding algorithm, and ML-Agents, a reinforcement learning-based system—within a Unity-based simulation environment. We conducted 100 controlled simulations under identical environmental conditions, setting average survival time, distance to the player, and number of obstacle collisions as quantitative performance indicators. The experimental results demonstrated that the ML-Agents model achieved superior adaptability and spatial utilization, maintaining longer escape durations and broader movement paths. In contrast, the NavMesh method produced more stable performance in precise control, particularly in obstacle avoidance. Through this study, we highlight the flexibility and learning capability of reinforcement learning-based AI in dynamic game scenarios and propose the importance of compensation design and environmental diversity for enhancing its behavioral realism. This research provides practical insights for developing adaptive NPCs that balance precision and adaptability in game AI design.
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목차
- Abstract
- 1. Introduction
- 2. Research Background
- 3. Implementation of Escape Strategy AI and Experimental Environment
- 4. Performance Comparison and Results Analysis
- 5. Conclusion
- References
참고문헌
참고문헌 신청최근 본 자료
UCI(KEPA) : I410-151-26-02-096290634
