43 citations · 140 across the 14 of their papers we have counts for
5 papers · 1 filter
A Brief Guide to Designing and Evaluating Human-Centered Interactive Machine Learning
Kory W. Mathewson, Patrick M. Pilarski
Interactive machine learning (IML) is a field of research that explores how to leverage both human and computational abilities in decision making systems. IML represents a collabor…
A Human-Centered Approach to Interactive Machine Learning
Kory W. Mathewson
The interactive machine learning (IML) community aims to augment humans' ability to learn and make decisions over time through the development of automated decision-making systems.…
Shaping the Narrative Arc: An Information-Theoretic Approach to Collaborative Dialogue
Kory W. Mathewson, Pablo Samuel Castro, Colin Cherry +2
We consider the problem of designing an artificial agent capable of interacting with humans in collaborative dialogue to produce creative, engaging narratives. In this task, the go…
Reinforcement Learning based Embodied Agents Modelling Human Users Through Interaction and Multi-Sensory Perception
Kory W. Mathewson, Patrick M. Pilarski
This paper extends recent work in interactive machine learning (IML) focused on effectively incorporating human feedback. We show how control and feedback signals complement each o…
Simultaneous Control and Human Feedback in the Training of a Robotic Agent with Actor-Critic Reinforcement Learning
Kory W. Mathewson, Patrick M. Pilarski
This paper contributes a preliminary report on the advantages and disadvantages of incorporating simultaneous human control and feedback signals in the training of a reinforcement…