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• The role of prediction in biological and neurological systems
• Identifying relevant sensory information, both across sensors and time (sensor bootstrapping)
• Representations spanning multiple spatio-temporal scales
  
• Signals to symbols, symbol grounding
• General issues of grounded knowledge representations: formats, capabilities, affordances, and limitations
  
• Reasoning and planning in terms of grounded knowledge
  
• Active perception guided by sensory-motor experience
• Construction of perceptual or motor control primitives
• Grounded state representations (PSRs, OOMs, etc)
• Dynamical / environmental models grounded in sensory-motor experience
• Learning algorithms for intelligent agents
• Learning in infants, going from sensory data to representations

The workshop will be comprised of invited talks by 5-6 of the top people from a variety of disciplines related to experience based knowledge representations. The speakers will share their area-specific knowledge and understanding of these issues with the workshop attendees. Several discussion sessions will give an opportunity for all workshop participants to discuss ideas. The workshop will conclude with a poster session populated with work submitted by the community at large.

A central goal is to bring together the perspectives of different communities.  We invite participants from any area, including machine learning, cognitive science, computational neuroscience, developmental robotics, and philosophy.

Dr. Richard Sutton

Richard S. Sutton is professor and iCORE chair of computing science at the University of Alberta.  He is a fellow of the American Association for Artificial Intelligence and co-author of the textbook Reinforcement Learning: An Introduction from MIT Press. Before joining the University of Alberta in 2003, he worked in industry at AT&T and GTE Labs, and in academia at the University of Massachusetts. He received a PhD in computer science from the University of Massachusetts in 1984 and a BA in psychology from Stanford University in 1978.  Rich's research interests center on the learning problems facing a decision-maker interacting with its environment, which he sees as central to artificial intelligence.  He is also interested in animal learning psychology, in connectionist networks, and generally in systems that continually improve their representations and models of the world.

Attached Documents

• Slides
  

Dr. Deb Roy

Deb Roy is an associate professor of media arts and sciences at the Massachusetts Institute of Technology, where he heads the Media Lab's Cognitive Machines research group. In 2003 he was appointed AT&T Career Development Professor. Roy has published over 50 peer-reviewed papers in the areas of artificial intelligence, cognitive modeling, data mining, robotics, and human-machine interface design. He has served as guest editor for the journal Artificial Intelligence, and as an associate of the journal Behavioral and Brain Sciences. Roy collaborates closely with industry in the areas of data visualization, data mining, and the design of human-machine collaborative systems. He holds a BASc in computer engineering from University of Waterloo, Canada, and MS and PhD degrees in media arts and sciences from MIT.

Dr. Mark H. Bickhard

Mark H. Bickhard received his B.S. in Mathematics, M.S. in Statistics, and Ph. D. in Human Development, all from the University of Chicago.  He taught at the University of Texas at Austin for eighteen years before joining Lehigh University in 1990 as Henry R. Luce Professor in Cognitive Robotics and the Philosophy of Knowledge.  He is affiliated with the Departments of Psychology, Philosophy, Biology, Counseling, and Computer Science, and is Director of the Institute for Interactivist Studies and of the Complex Systems Research Group.  He was Director of Cognitive Science from 1992 thru 2003.  His work focuses on the nature and development of persons, as biological, psychological, and social beings.  This work has generated an integrated organization of models encompassing the whole person, ranging from the nature of biological function through perception, cognition, processes of and constraints on development, rationality, emotions, reflexive consciousness, language, psychopathology, and the relationships between the emergence of social reality and the social ontology of persons.

Dr. Rajesh Rao

Rajesh Rao is an associate professor in the Computer Science and Engineering department at the University of Washington, where he heads the Laboratory for Neural Systems. He received his PhD from the University of Rochester and was a Sloan Postdoctoral Fellow at the Salk Institute for Biological Studies before joining the University of Washington. His research spans the areas of computational neuroscience, humanoid robotics, and brain-computer interfaces. He is the recipient of a David and Lucile Packard Fellowship, an Alfred P. Sloan Fellowship for junior faculty, an ONR Young Investigator Award, and an NSF Career award. He is the co-editor of two books: Probabilistic Models of the Brain (2002) and Bayesian Brain (2007).

Dr. Bernard Balleine

Bernard Balleine is Professor in the Department of Psychology and Associate Director of the Brain Research Institute, UCLA. He received his BA from the University of Sydney, Australia and his PhD from the University of Cambridge, UK where he was subsequently elected a Research Fellow of Jesus College. His research focuses on the motivational, cognitive and neural determinants of goal-directed action as a part of the larger goal of establishing the fundamental distinctions between reflexive, volitional and habitual actions.

Attached Documents

Confirmed Posters

Click through these links to see abstract, download of paper and/or poster, and additional authors comments.

NOTE: If you had a poster at the workshop, and it's not listed here, please e-mail brian@tannerpages.com

The Special Thing About Experience Is That It Has A Now - Anna Koop and Richard Sutton

POMDP Homomorphisms - Alicia Peregrin Wolfe.

Neighborhood Components Analysis for Reward-Based Dimensionality Reduction - Nathan Sprague

Five Basic Principles of Developmental Robotics - Alexander Stoytchev

State Similarity Based Approach for Improving Performance in RL - Sertan Girgin, Faruk Polat and Reda Alhajj

Human State Estimation Through Learning Over Common Sense Data - William Pentney & Matthai Philipose & Jeff Bilmes & Henry Kautz

Social interaction through movement: concepts from perception-action interplay - Emilia I. Barakova

Learning Subjective Representations Through Dimensionality Reduction -Dana Wilkinson and Michael Bowling and Ali Ghodsi

An Information Theoretic Approach for Building Approximate Predictive Models - Susanna Still, Monica Dinculescu, Doina Precup

All submissions should be emailed to grounded.workshop@gmail.com

Please direct all questions and submissions to grounded.workshop@gmail.com

The official workshop website is hosted by RLAI at: http://rlai.cs.ualberta.ca/RLAI/prw2006.html

This workshop is in the same spirit as recent workshops including:

• 2006 Barbados workshop Predictive State Representations
• 2005 Barbados workshop Predictive State Representations
• 2004 ICML Workshop Predictive Representations of World Knowledge
• 2001 AAAI Spring Symposium Learning Grounded Representations