Cart (Loading....) | Create Account
Close category search window
 

Autonomous science platforms and question-asking machines

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Knuth, K.H. ; Depts. of Phys. & Inf., Univ. at Albany (SUNY), Albany, NY, USA ; Center, J.L.

As we become increasingly reliant on remote science platforms, the ability to autonomously and intelligently perform data collection becomes critical. In this paper we view these platforms as question-asking machines and introduce a paradigm based on the scientific method, which couples the processes of inference and inquiry to form a model-based learning cycle. Unlike modern autonomous instrumentation, the system is not programmed to collect data directly, but instead, is programmed to learn based on a set of models. Computationally, this learning cycle is implemented in software consisting of a Bayesian probability-based inference engine coupled to an entropy-based inquiry engine. Operationally, a given experiment is viewed as a question, whose relevance is computed using the inquiry calculus, which is a natural order-theoretic generalization of information theory. In simple cases, the relevance is proportional to the entropy. This data is then analyzed by the inference engine, which updates the state of knowledge of the instrument. This new state of knowledge is then used as a basis for future inquiry as the system continues to learn. This paper will introduce the learning methodology, describe its implementation in software, and demonstrate the process with a robotic explorer that autonomously and intelligently performs data collection to solve a search-and-characterize problem.

Published in:

Cognitive Information Processing (CIP), 2010 2nd International Workshop on

Date of Conference:

14-16 June 2010

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.