Search Results for author: Adam S. R. Parker

Found 5 papers, 0 papers with code

The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents

no code implementations17 Mar 2022 Patrick M. Pilarski, Andrew Butcher, Elnaz Davoodi, Michael Bradley Johanson, Dylan J. A. Brenneis, Adam S. R. Parker, Leslie Acker, Matthew M. Botvinick, Joseph Modayil, Adam White

Our results showcase the speed of learning for Pavlovian signalling, the impact that different temporal representations do (and do not) have on agent-agent coordination, and how temporal aliasing impacts agent-agent and human-agent interactions differently.

Decision Making reinforcement-learning

Pavlovian Signalling with General Value Functions in Agent-Agent Temporal Decision Making

no code implementations11 Jan 2022 Andrew Butcher, Michael Bradley Johanson, Elnaz Davoodi, Dylan J. A. Brenneis, Leslie Acker, Adam S. R. Parker, Adam White, Joseph Modayil, Patrick M. Pilarski

We further show how to computationally build this adaptive signalling process out of a fixed signalling process, characterized by fast continual prediction learning and minimal constraints on the nature of the agent receiving signals.

Decision Making reinforcement-learning

Learned human-agent decision-making, communication and joint action in a virtual reality environment

no code implementations7 May 2019 Patrick M. Pilarski, Andrew Butcher, Michael Johanson, Matthew M. Botvinick, Andrew Bolt, Adam S. R. Parker

In this work, we contribute a virtual reality environment wherein a human and an agent can adapt their predictions, their actions, and their communication so as to pursue a simple foraging task.

Decision Making

Communicative Capital for Prosthetic Agents

no code implementations10 Nov 2017 Patrick M. Pilarski, Richard S. Sutton, Kory W. Mathewson, Craig Sherstan, Adam S. R. Parker, Ann L. Edwards

This work presents an overarching perspective on the role that machine intelligence can play in enhancing human abilities, especially those that have been diminished due to injury or illness.

Using Learned Predictions as Feedback to Improve Control and Communication with an Artificial Limb: Preliminary Findings

no code implementations8 Aug 2014 Adam S. R. Parker, Ann L. Edwards, Patrick M. Pilarski

Our study therefore contributes initial evidence that prediction learning and machine intelligence can benefit not just control, but also feedback from an artificial limb.

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