Title

Reinforcement Learning and the Frame Problem

Published In

Proceedings of the International Joint Conference on Neural Networks

Document Type

Citation

Publication Date

12-1-2005

Abstract

The Frame Problem, originally proposed within AI, has grown to be a fundamental stumbling block for building intelligent agents and modeling the mind. The source of the frame problem stems from the nature of symbolic processing. Unfortunately, connectionist approaches have long been criticized as having weaker representational capabilities than symbolic systems so have not been considered by many. The equivalence between the representational power of symbolic systems and connectionist architectures is redressed through neural manifolds, and reveals an associated frame problem. Working within the construct of neural manifolds, the frame problem is solved through the use of contextual reinforcement learning, a new paradigm recently proposed.

Locate the Document

https://doi.org/10.1109/IJCNN.2005.1556398

DOI

10.1109/IJCNN.2005.1556398

Persistent Identifier

https://archives.pdx.edu/ds/psu/37322

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