CAN: Case-based reasoning in an adversarial non-player character

Authors

Anne Marie M. Africa, De La Salle University, Manila
Karmela Angela G. Cortez, De La Salle University, Manila
Gian Kristian A. Fontanilla, De La Salle University, Manila
Paul Michael O. Go, De La Salle University, Manila
Danny C. Cheng, De La Salle University, Manila

College

College of Computer Studies

Department/Unit

Software Technology

Document Type

Archival Material/Manuscript

Abstract

Game development, which was primarily driven by the desire to achieve realistic graphics, is now turning to other innovations such as applying Artificial Intelligence (AI) techniques (Cunningham, et al., 2003). AI algorithms of heuristic nature such as the A* path finding algorithm, A-Life/flocking algorithms, and Fuzzy State Machines (FSMs) are well understood in the context of computer games (Cunningham et al, 2001). However, such algorithms only make use of simple decision making and still lack the ability to learn (Bernon). One type of game that requires this kind of learning is real-time strategy games. This research intends to design an adversarial Non Player Characters (NPC) that learns strategies in a real time strategy (RTS) game.

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Recommended Citation

Africa, A. M., Cortez, K. G., Fontanilla, G. A., Go, P. O., & Cheng, D. C. (2022). CAN: Case-based reasoning in an adversarial non-player character. Retrieved from https://animorepository.dlsu.edu.ph/faculty_research/5168

Disciplines

Computer Sciences

Keywords

Artificial intelligence; Games of strategy (Mathematics); Game theory—Computer programs

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