Title

Experience-Based Identification and Control via Higher-Level Learning And Context Discernment

Published In

IEEE International Conference on Neural Networks - Conference Proceedings

ISBN

0780394909,9780780394902

Document Type

Citation

Publication Date

1-1-2006

Abstract

In AI systems so far developed, more knowledge (typically stored as "rules") entails slower processing; in the case of humans, the more knowledge attained (in the form of experience), the speed/efficiency of performing new related tasks is improved. Experience-Based (EB) Identification and Control is explored with the objective of achieving more human-like processes for 'intelligent' computing Agents. The notion of experience is being successfully addressed via a novel concept for applying Reinforcement Learning (RL), called HLLA - Higher Level Learning Algorithm. The key idea is to re-purpose the RL method (to a "higher level") such that instead of creating an optimal controller for a given task, an already achieved collection of such solutions for a variety of related contexts is provided (as an experience repository), and HLLA creates a strategy for optimally selecting a solution from the repository. The selection process is triggered by the Agent becoming aware that a change in context has occurred, followed by the Agent seeking information about what changed - a process here called context discernment - and finally, by selection. Typically, context discernment entails a form of system identification (SID); substantial enhancement of SID is also achieved via the EB methods. Examples are given. © 2006 IEEE.

Locate the Document

https://doi.org/10.1109/CIISP.2007.369204

DOI

10.1109/ijcnn.2006.247279

Persistent Identifier

https://archives.pdx.edu/ds/psu/37285

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