BibTeX
@MISC{Santiago_i.background,
author = {Roberto Santiago and George G. Lendaris},
title = {I. Background of the Frame Problem},
year = {}
}
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Abstract
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, connec-tionist approaches have long been criticized as having weaker representational capabilities than symbolic sys-tems so have not been considered by many. The equiva-lence 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.
Keyphrases
frame problem neural manifold representational capability associated frame problem new paradigm connec-tionist approach connectionist architecture symbolic system contextual reinforcement learning fundamental stumbling block symbolic processing symbolic sys-tems building intelligent agent representational power
