Research and Development

Observational Learning


Observational Learning by Albert Bandura, Social Learning Theory by Albert Bandura and Richard H. Walters and Perspectives on Observational Learning in Animals by Thomas R. Zentall.

Programming by Demonstration

Robot Learning from Human Teachers by Sonia Chernova and Andrea L. Thomaz, Robot Learning from Demonstration by Christopher G. Atkeson and Stefan Schaal, A Survey of Robot Learning from Demonstration by Brenna D. Argall, Sonia Chernova, Manuela Veloso and Brett Browning, Robot Programming by Demonstration by Aude Billard, Sylvain Calinon, Ruediger Dillmann and Stefan Schaal, Programming by Demonstration: A Taxonomy of Current Relevant Methods to Teach and Describe New Skills to Robots by Jordi Bautista-Ballester, Jaume Vergés-Llahí and Domenec Puig and Learning Procedural Knowledge through Observation by Michael van Lent and John E. Laird.

Interactive Task Learning

Interactive Task Learning by John E. Laird, Kevin A. Gluck, John Anderson, Kenneth D. Forbus, Odest C. Jenkins, Christian Lebiere, Dario Salvucci, Matthias Scheutz, Andrea Thomaz, Greg Trafton, Robert E. Wray, Shiwali Mohan and James R. Kirk and Interactive Task Learning: Humans, Robots, and Agents Acquiring New Tasks through Natural Interactions edited by Kevin A. Gluck and John E. Laird.

Multi-agent Systems

Multi-agent Reinforcement Learning: Independent vs. Cooperative Agents by Ming Tan, The Dynamics of Reinforcement Learning in Cooperative Multiagent Systems by Caroline Claus and Craig Boutilier, Implicit Imitation in Multiagent Reinforcement Learning by Bob Price and Craig Boutilier, Observational Learning by Reinforcement Learning by Diana Borsa, Nicolas Heess, Bilal Piot, Siqi Liu, Leonard Hasenclever, Remi Munos and Olivier Pietquin, Learning in Multi-agent Systems by Eduardo Alonso, Mark D'inverno, Daniel Kudenko, Michael Luck and Jason Noble and Learning to Teach in Cooperative Multiagent Reinforcement Learning by Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu, Gerald Tesauro, Matthew Riemer, Christopher Amato, Murray Campbell and Jonathan P. How.