Computational Approaches to Representation Change during Learning and Development
Papers from the 2007 AAAI Fall Symposium
Clayton T. Morrison and Tim Oates, Program Cochairs
Technical Report FS-07-03. Published by The AAAI Press, Menlo Park, California
This technical report is also available in book and CD format.
Please Note: Abstracts are linked to individual titles, and will appear in a separate browser window. Full-text versions of the papers are linked to the abstract text. Access to full text may be restricted to AAAI members. PDF file sizes may be large!
Contents
Organizing Committee / 1
Clayton T. Morrison and Tim Oates
Children's Rational Exploration / 1
Elizabeth Baraff Bonawitz, Laura Schulz
Handling Representation Changes by Autistic Reasoning / 9
Boris Galitsky
Representing Systems with Hidden State / 17
Christopher Hundt, Prakash Panagaden, Joelle Pineau, Doina Precup
Representation Discovery in Planning using Harmonic Analysis / 24
Jeff Johns, Sarah Osentoski, Sridhar Mahadevan
Handling Granularity Differences in Knowledge Integration / 32
Doo Soon Kim, Bruce Porter
Autonomous Robot Learning of Foundational Representations / 39
Benjamin Kuipers
Changing Semantic Role Representations with Holographic Memory / 40
Simon D. Levy
Exploring Massive Learning via a Prediction System / 46
Omid Madani
Prediction Games in Infinitely Rich Worlds / 54
Omid Madani
Representation Change in the Marchitecture / 56
Marc Pickett I, Don Miner
Automatic Development from Pixel-level Representation to Action-level Representation in Robot Navigation / 58
Jefferson Provost
Computational Models for Representation Change in Human Learning / 60
Jennifer Roberts
Representational Reformulation in Hypothesis-Driven Recognition / 62
Benjamin Rode, Robert C. Kahlert
Diversity of Developmental Trajectories in Natural and Artificial Intelligence / 70
Aaron Sloman
Representation Transfer for Reinforcement Learning / 78
Matthew E. Taylor, Peter Stone
A Multiple Representation Approach to Learning Dynamical Systems / 86
Thomas J. Walsh, Michael L. Littman
Relational State-Space Feature Learning and Its Applications in Planning / 88
Jia-Hong Wu, Robert Givan
AAAI Digital Library
AAAI relies on your generous support through membership and donations. If you find these resources useful, we would be grateful for your support.