Reinforcement Learning and the Frame Problem
This work was partially supported by NSF Gran tECS-0301022 ,and IEEE Walter Karplus Summer Grant,
Proceedings of the International Joint Conference on Neural Networks
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.
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Santiago, R., & Lendaris, G. G. (2005, July). Reinforcement learning and the frame problem. In Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. (Vol. 5, pp. 2971-2976). IEEE.