Self-Organizing Perceptual and Temporal Abstraction (2004)
Jefferson Provost, Benjamin J. Kuipers, Risto Miikkulainen
A major current challenge in reinforcement learning research is to extend methods that work well on discrete, short-range, low-dimensional problems to continuous, highdiameter, high-dimensional...
Modeling cortical maps with Topographica (2003)
James A. Bednar, Yoonsuck Choe, Judah De Paula, Risto Miikkulainen, Jefferson Provost
The biological function of cortical neurons can often be understood only in the context of large, highly interconnected networks. These networks typically form two-dimensional topographic maps, such...
Exploiting Local Perceptual Models for Topological Map-Building (2003)
Patrick Beeson, Matt Macmahon, Joseph Modayil, Jefferson Provost, Francesco Savelli, Benjamin Kuipers
The Spatial Semantic Hierarchy (SSH) provides a robot-independent ontology and logical theory for building topological maps of large-scale environments online. Existing SSH implementations make very...
Jonathan Cohen, Brian Macwhinney, Matthew Flatt, Jefferson Provost
PsyScope is an integrated environment for designing and running psychology experiments on Macintosh computers. The primary goal of PsyScope is to give psychology students and trained researchers,...
Naïve-Bayes vs. Rule-Learning in Classification of Email (2002)
Recent growth in the use of email for communication and the corresponding growth in the volume of email received has made automatic processing of email desirable. Two learning methods, nave bayesian...
Presented at the AAAI-2001 Spring Symposium on Learning Grounded Representations. (2002)
Jefferson Provost, Patrick Beeson, Benjamin J. Kuipers
The Spatial Semantic Hierarchy (SSH) is a multi-level representation of the cognitive map used for navigation in largescale space. We propose a method for learning a portion of this representation,...
Learning from Uninterpreted Experience in the SSH (2001)
Benjamin Kuipers, Patrick Beeson, Joseph Modayil, Jefferson Provost
blems. # Feature learning. Learn a hierarchy of features defined on top of the raw sensory features, and identify reliable relationships among features, and between features and motor signals. #...
Toward Learning the Causal Layer of the Spatial Semantic Hierarchy using SOMs (2001)
Jefferson Provost, Patrick Beeson, Benjamin J. Kuipers
The Spatial Semantic Hierarchy (SSH) is a multi-level representation of the cognitive map used for navigation in largescale space. We propose a method for learning a portion of this representation,...
Presented at the AAAI-2001 Spring Symposium on Learning Grounded Representations. (2001)
Jefferson Provost, Patrick Beeson, Benjamin J. Kuipers
The Spatial Semantic Hierarchy (SSH) is a multi-level representation of the cognitive map used for navigation in largescale space. We propose a method for learning a portion of this representation,...
Design Jonathan, D. Cohen, Brian Macwhinney, Development Jefferson, Provost Matthew Flatt, Matthew Cushman, ...
Table of Contents i Overview of the PsyScope Manual 0.1 Organization 1 0.2 System Requirements 1 0.3 Conventions 2 Part 1: Introduction to PsyScope Chapter 1. Introduction 1 Chapter 2. Running Your...
Robotic Sensor-view Recognition with Neural Networks (1999)
I present methods for using neural-networks to recognize sensor views from a laser range-finder for robotic navigation. First, I show that a simple back-propagation network can form a highly accurate...