Discovering and characterizing Hidden Variables

DOI

10.2991/agi.2010.7How to use a DOI?

Abstract

Theoretical entities are aspects of the world that cannot be sensed directly but that nevertheless are causally relevant. Scientifc inquiry has uncovered many such entities, such as black holes and dark matter. We claim that theoretical entities are omportant for the development of concepts within the lifetime of an individual, and present a novel neural network architecture that solves three problems related to theoretical entities: (1) discovering that they exist, (2) determining their number, and (3) computing their values. Experiments show the utility of the proposed approach using a discrete time dynamical system in which some of the state variables are hidden, and sensor data obtained from the camera of a mobile robot in which the sizes and locations of ob jects in the visual eld are observed but their sizes and locations (distances) in the three-dimensional world are not.

Copyright

© 2010, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Soumi Ray
AU  - Tim Oates
PY  - 2010/06
DA  - 2010/06
TI  - Discovering and characterizing Hidden Variables
BT  - Proceedings of the 3d Conference on Artificial General Intelligence (2010)
PB  - Atlantis Press
SP  - 29
EP  - 34
SN  - 1951-6851
UR  - https://doi.org/10.2991/agi.2010.7
DO  - 10.2991/agi.2010.7
ID  - Ray2010/06
ER  -