56 citations · 265 across the 35 of their papers we have counts for
38 papers
Idiobionics: The Unification of Privacy and Intelligent Robotic Prostheses
Kwesi Afari Darfoor, Patrick M. Pilarski, Bailey Kacsmar
The human body is at the center of a growing family of technologies designed to tightly and persistently couple biological and digital systems. Robotic prostheses are a representat…
Primate-like perceptual decision making emerges through deep recurrent reinforcement learning
Nathan J. Wispinski, Scott A. Stone, Anthony Singhal +2
Progress has led to a detailed understanding of the neural mechanisms that underlie decision making in primates. However, less is known about why such mechanisms are present in the…
Hierarchical Reinforcement Learning Framework for Adaptive Walking Control Using General Value Functions of Lower-Limb Sensor Signals
Sonny T. Jones, Grange M. Simpson, Patrick M. Pilarski +1
Rehabilitation technology is a natural setting to study the shared learning and decision-making of human and machine agents. In this work, we explore the use of Hierarchical Reinfo…
Continually Learned Pavlovian Signalling Without Forgetting for Human-in-the-Loop Robotic Control
Adam S. R. Parker, Michael R. Dawson, Patrick M. Pilarski
Artificial limbs are sophisticated devices to assist people with tasks of daily living. Despite advanced robotic prostheses demonstrating similar motion capabilities to biological…
Joint Action is a Framework for Understanding Partnerships Between Humans and Upper Limb Prostheses
Michael R. Dawson, Adam S. R. Parker, Heather E. Williams +4
Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by the robotic limb. However, the method for controlling multiple…
Five Properties of Specific Curiosity You Didn't Know Curious Machines Should Have
Nadia M. Ady, Roshan Shariff, Johannes Günther +1
Curiosity for machine agents has been a focus of lively research activity. The study of human and animal curiosity, particularly specific curiosity, has unearthed several propertie…
Over-communicate no more: Situated RL agents learn concise communication protocols
Aleksandra Kalinowska, Elnaz Davoodi, Florian Strub +5
While it is known that communication facilitates cooperation in multi-agent settings, it is unclear how to design artificial agents that can learn to effectively and efficiently co…
Adaptive patch foraging in deep reinforcement learning agents
Nathan J. Wispinski, Andrew Butcher, Kory W. Mathewson +3
Patch foraging is one of the most heavily studied behavioral optimization challenges in biology. However, despite its importance to biological intelligence, this behavioral optimiz…
The Alberta Plan for AI Research
Richard S. Sutton, Michael Bowling, Patrick M. Pilarski
Herein we describe our approach to artificial intelligence research, which we call the Alberta Plan. The Alberta Plan is pursued within our research groups in Alberta and by others…
What Should I Know? Using Meta-gradient Descent for Predictive Feature Discovery in a Single Stream of Experience
Alexandra Kearney, Anna Koop, Johannes Günther +1
In computational reinforcement learning, a growing body of work seeks to construct an agent's perception of the world through predictions of future sensations; predictions about en…
Prototyping three key properties of specific curiosity in computational reinforcement learning
Nadia M. Ady, Roshan Shariff, Johannes Günther +1
Curiosity for machine agents has been a focus of intense research. The study of human and animal curiosity, particularly specific curiosity, has unearthed several properties that w…
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…
The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents
Patrick M. Pilarski, Andrew Butcher, Elnaz Davoodi +7
Learned communication between agents is a powerful tool when approaching decision-making problems that are hard to overcome by any single agent in isolation. However, continual coo…
Pavlovian Signalling with General Value Functions in Agent-Agent Temporal Decision Making
Andrew Butcher, Michael Bradley Johanson, Elnaz Davoodi +6
In this paper, we contribute a multi-faceted study into Pavlovian signalling -- a process by which learned, temporally extended predictions made by one agent inform decision-making…
Assessing Human Interaction in Virtual Reality With Continually Learning Prediction Agents Based on Reinforcement Learning Algorithms: A Pilot Study
Dylan J. A. Brenneis, Adam S. Parker, Michael Bradley Johanson +7
Artificial intelligence systems increasingly involve continual learning to enable flexibility in general situations that are not encountered during system training. Human interacti…
Finding Useful Predictions by Meta-gradient Descent to Improve Decision-making
Alex Kearney, Anna Koop, Johannes Günther +1
In computational reinforcement learning, a growing body of work seeks to express an agent's model of the world through predictions about future sensations. In this manuscript we fo…
Document-editing Assistants and Model-based Reinforcement Learning as a Path to Conversational AI
Katya Kudashkina, Patrick M. Pilarski, Richard S. Sutton
Intelligent assistants that follow commands or answer simple questions, such as Siri and Google search, are among the most economically important applications of AI. Future convers…
What's a Good Prediction? Challenges in evaluating an agent's knowledge
Alex Kearney, Anna Koop, Patrick M. Pilarski
Constructing general knowledge by learning task-independent models of the world can help agents solve challenging problems. However, both constructing and evaluating such models re…
Gamma-Nets: Generalizing Value Estimation over Timescale
Craig Sherstan, Shibhansh Dohare, James MacGlashan +2
We present -nets, a method for generalizing value function estimation over timescale. By using the timescale as one of the estimator's inputs we can estimate value for arbitrary…
Examining the Use of Temporal-Difference Incremental Delta-Bar-Delta for Real-World Predictive Knowledge Architectures
Johannes Günther, Nadia M. Ady, Alex Kearney +2
Predictions and predictive knowledge have seen recent success in improving not only robot control but also other applications ranging from industrial process control to rehabilitat…
Learned human-agent decision-making, communication and joint action in a virtual reality environment
Patrick M. Pilarski, Andrew Butcher, Michael Johanson +3
Humans make decisions and act alongside other humans to pursue both short-term and long-term goals. As a result of ongoing progress in areas such as computing science and automatio…
Interpretable PID Parameter Tuning for Control Engineering using General Dynamic Neural Networks: An Extensive Comparison
Johannes Günther, Elias Reichensdörfer, Patrick M. Pilarski +1
Modern automation systems rely on closed loop control, wherein a controller interacts with a controlled process, based on observations. These systems are increasingly complex, yet…
When is a Prediction Knowledge?
Alex Kearney, Patrick M. Pilarski
Within Reinforcement Learning, there is a growing collection of research which aims to express all of an agent's knowledge of the world through predictions about sensation, behavio…
Learning Gentle Object Manipulation with Curiosity-Driven Deep Reinforcement Learning
Sandy H. Huang, Martina Zambelli, Jackie Kay +4
Robots must know how to be gentle when they need to interact with fragile objects, or when the robot itself is prone to wear and tear. We propose an approach that enables deep rein…