The mastery of skills such as playing tennis or balancing an inverted pendulum implies a very accurate control of movements to achieve the task goals.
There are a range of metrics that can be applied to the artifacts produced by procedural content generation, and several of them come with qualitative claims.
This article outlines what we learned from the first year of the AI Settlement Generation Competition in Minecraft, a competition about producing AI programs that can generate interesting settlements in Minecraft for an unseen map.
Live Action Role-Playing (LARP) games and similar experiences are becoming a popular game genre.
We found that Warmth and Competence, among all RoSAS and Godspeed dimensions, are the most important predictors for human preferences between different robot behaviors.
A challenge in using robots in human-inhabited environments is to design behavior that is engaging, yet robust to the perturbations induced by human interaction.
Deep reinforcement learning has learned to play many games well, but failed on others.
We introduce the Chronicle Challenge as an optional addition to the Settlement Generation Challenge in Minecraft.
The extent to which these games benchmark consist of fair competition between human and AI is also a matter of debate.
This paper introduces an information-theoretic method for selecting a subset of problems which gives the most information about a group of problem-solving algorithms.
Research on intrinsic motivations may profit from an additional way to implement intrinsically motivated agents that also share the biological plausibility of active inference.
Creating Non-Player Characters (NPCs) that can react robustly to unforeseen player behaviour or novel game content is difficult and time-consuming.
This paper introduces the settlement generation competition for Minecraft, the first part of the Generative Design in Minecraft challenge.
Models of intrinsic motivation present an important means to produce sensible behaviour in the absence of extrinsic rewards.
In this paper we investigate how the information-theoretic measure of agent empowerment can provide a task-independent, intrinsic motivation to restructure the world.
This book chapter is an introduction to and an overview of the information-theoretic, task independent utility function "Empowerment", which is defined as the channel capacity between an agent's actions and an agent's sensors.