Go-Blend behavior and affect

TL;DR

This paper introduces a novel affect modeling framework using reinforcement learning, specifically a modified Go-Explore algorithm, to create agents that can mimic human affect and behavior in gaming environments, enhancing AI believability.

Contribution

It presents a new paradigm for affective computing by integrating behavior and affect through reinforcement learning, and demonstrates its application in game testing with agents capable of diverse affective expressions.

Findings

Agents can mimic human arousal levels effectively.

The framework enables blending of behavioral and affective patterns.

Enhanced AI believability in game testing environments.

Abstract

This paper proposes a paradigm shift for affective computing by viewing the affect modeling task as a reinforcement learning process. According to our proposed framework the context (environment) and the actions of an agent define the common representation that interweaves behavior and affect. To realise this framework we build on recent advances in reinforcement learning and use a modified version of the Go-Explore algorithm which has showcased supreme performance in hard exploration tasks. In this initial study, we test our framework in an arcade game by training Go-Explore agents to both play optimally and attempt to mimic human demonstrations of arousal. We vary the degree of importance between optimal play and arousal imitation and create agents that can effectively display a palette of affect and behavioral patterns. Our Go-Explore implementation not only introduces a new paradigm for affect modeling; it empowers believable AI-based game testing by providing agents that can blend and express a multitude of behavioral and affective patterns.