Flow is a subjective state characterized by immersion and engagement in one's current
activity. The benefits of flow for productivity and health are well-documented, but a rigorous …

One difficulty in using artificial agents for human-assistive applications lies in the challenge
of accurately assisting with a person's goal (s). Existing methods tend to rely on inferring the …

We review the problem of defining and inferring a state for a control system based on
complex, high-dimensional, highly uncertain measurement streams, such as videos. Such a …

For an AI agent to make trustworthy decision recommendations under uncertainty on behalf
of human principals, it should be able to explain why its recommended decisions make …

This paper presents a review of intrinsic motivation in player modeling, with a focus on
simulation-based game testing. Modern AI agents can learn to win many games; from a …

This paper presents a reinforcement learning approach to synthesizing task-driven control
policies for robotic systems equipped with rich sensory modalities (eg, vision or depth) …

For an AI agent to make trustworthy decision recommendations under uncertainty on behalf
of human principals, it should be able to explain why its recommended decisions make …

Intrinsically motivated artificial agents learn advantageous behavior without externally-
provided rewards. Previously, it was shown that maximizing mutual information between …

Answering this question requires a mechanistic understanding of how the experience of
immersion and engagement—commonly known as flow (Csikszentmihalyi, 1975, 1990; …

Mutual Information between agent Actions and environment States (MIAS) quantifies the
influence of agent on its environment. Recently, it was found that the maximization of MIAS …