My dissertation on Cognitive Economy and the Role of Representation in On-Line Learning addresses the question `How can an intelligent agent learn to categorize its perceptions according to its goals.' The abstract page contains the abstract and has links to the Introduction and to an Afterword giving the big picture. The full dissertation is also available.
The dissertation is based on the idea that we can simplify the state-space for a problem by learning to filter out irrelevant information. It applied the psychological principle of cognitive economy to the domain of reinforcement learning to develop objective criteria for representational adequacy. Then it proved that these criteria allow tasks to be learned, and presented case studies of a system based on these ideas that successfully learned its representation along with its task. This work grounds feature relevance on principled criteria regarding the rewards associated with particular actions.
My recent paper When Do Differences Matter? On-Line Feature Extraction Through Cognitive Economy describes how cognitive economy may be worked out in a reinforcement learning system, and presents experimental studies based on the puck-on-a-hill task. Contributions of this paper include a description of how the idea of cognitive economy addresses the open problem of knowing how accurately we need to learn the action values, and a new methodology for testing and camparing representations by means of learning curves. [Paper now accepted for publication by Cognitive Systems Research, in press]
|Last modified: March 12, 2006|