Adaptation of an adversarial non-player character through case based reasoning

• Main Authors: Cheng, Danny C., Fontanilla, Gian Kristian A., Africa, Anne Marie M., Cortez, Karmela Angela G., Go, Paul Michael O.
• Format: text
• Published: Animo Repository 2022
• Subjects: Artificial intelligence; Games of strategy (Mathematics); Game theory—Computer programs; Artificial Intelligence and Robotics
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/5165
• Institution: De La Salle University

Game development is now turning to other innovations such as applying Artificial Intelligence (AI) techniques [4]. However, such algorithms only make use of simple decision making and still lack the ability to learn [2] One type of game that requires this kind of learning is real time strategy games. This research intends to present the CAN system that is designed as an adversarial Non Player Characters (NPC) that learns strategies in a real time strategy (RTS) game using Case-based Reasoning. Using strategies learned from the past actions of the human player. CAN is able to adapt to current situation and change strategy online.