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Open-Ended Evolution for Minecraft Building Generation
To realize this goal we evaluate individuals' novelty in the latent space using a 3D autoencoder, and alternate between phases of exploration and transformation.
Play with Emotion: Affect-Driven Reinforcement Learning
According to the proposed paradigm, RL agents learn a policy (i. e. affective interaction) by attempting to maximize a set of rewards (i. e. behavioral and affective patterns) via their experience with their environment (i. e. context).
Generative Personas That Behave and Experience Like Humans
Using artificial intelligence (AI) to automatically test a game remains a critical challenge for the development of richer and more complex game worlds and for the advancement of AI at large.
Go-Blend behavior and affect
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.
