AI for dynamic team-mate adaptation in games

There is a long tradition of developing games in which the difficulty level is dynamically adapted to the performance of human players. However, there has been less work on the creation of game systems that perform dynamic team-mate adaption - and even less on developing team-mate NPCs (Non Player Characters) that adaptively support players in the face of opponents that adaptively increase the difficulty for the player. This paper is based on preliminary research to identify the key elements involved in developing “buddy” NPC team-mates that dynamically adapt to the needs and behaviors of human players while cooperating to compete against adaptive AI opponents. We discuss the computational and design challenges involved in developing such agents in the context of a simple test game called Capture the Gunner (CTG). The main contributions of the paper include: a proposed vocabulary and framework for understanding/modeling team-mate systems with adaptive difficulty, a particular technique for adaptive team-mate cooperation in the face of an adaptive opponent, and the identification of several significant new issues that arise in the process of developing computer games that involve adaptive NPC team-mates that cooperate with the player in the face of adaptive opponents.