Building Markov Decision Process based models of remote experimental setups for state evaluation

Maiti, Ananda and Kist, Alexander A. and Maxwell, Andrew D. (2015) Building Markov Decision Process based models of remote experimental setups for state evaluation. In: 2015 IEEE Symposium Series on Computational Intelligence (SSCI), 7-10 Dec 2015, Cape Town, South Africa.

Abstract

Remote Access Laboratories (RAL) are online environments that allows the users to interact with instruments through the Internet. RALs are governed by a Remote Laboratory Management System (RLMS) that provides the specific control technology and control policies with regards to an experiment and the corresponding hardware. Normally, in a centralized RAL these control strategies and policies are created by the experiment providers in the RLMS. In a distributed Peer-to-Peer RAL scenario, individual users designing their own rigs and are incapable of producing and enforcing the control policies to ensure safe and stable use of the experimental rigs. Thus the experiment controllers in such a scenario have to be smart enough to learn and enforce those policies. This paper discusses a method to create Markov’s Decision Process from the user’s interactions with the experimental rig and use it to ensure stability as well as support other users by evaluating the current state of the rig in their experimental session.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Papers in which all authors are employed by a Crown government (UK, Canada, and Australia), the copyright notice is: ©2015 Crown.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering
Date Deposited: 09 Jun 2016 23:07
Last Modified: 05 Jul 2016 01:45
Uncontrolled Keywords: remote laboratories; markov's decision process; smart devices; micro-controllers; supervised learning
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
Socio-Economic Objective: C Society > 93 Education and Training > 9302 Teaching and Instruction > 930203 Teaching and Instruction Technologies
URI: http://eprints.usq.edu.au/id/eprint/28656

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