Experience-Based Identification and Control via Higher-Level Learning And Context Discernment
This work was supported in part by the NSF Grant no. ECS-0301022.
IEEE International Conference on Neural Networks - Conference Proceedings
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
Lendaris, G. G. (2006, July). Experience-based identification and control via higher-level learning and context discernment. In The 2006 IEEE International Joint Conference on Neural Network Proceedings (pp. 5251-5258). IEEE.