Cited by 1,451Reinforcement Learning in RoboticsNeural Networks and ApplicationsMotor Control and Adaptation

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Forward Models: Supervised Learning with a Distal Teacher

Jordan, Michael I., Rumelhart, David E.·1992·Cognitive Science

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Abstract

Internal models of the environment have an important role to play in adaptive systems, in general, and are of particular importance for the supervised learning paradigm. In this article we demonstrate that certain classical problems associated with the notion of the “teacher” in supervised learning can be solved by judicious use of learned internal models as components of the adaptive system. In particular, we show how supervised learning algorithms can be utilized in cases in which an unknown dynamical system intervenes between actions and desired outcomes. Our approach applies to any supervised learning algorithm that is capable of learning in multilayer networks.

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Authors: Jordan, M. I., & Rumelhart, D. E.
Affiliations: Massachusetts Institute of Technology (US)
Stanford University (US)
Corresponding author: Jordan, Michael I.
ORCID: Jordan, Michael I.
Venue: Cognitive Science
Publisher: Wiley
Published: 1992-07
Volume: 16
Issue: 3
Pages: 307-354
ISSN: 0364-0213 (ISSN-L)
1551-6709
DOI: 10.1207/s15516709cog1603_1
OpenAlex: W2043968544
Cited by: 1,451
References: 54
Citations by year: 2026: 18 · 2025: 15 · 2024: 33 · 2023: 47 · 2022: 40 · 2021: 40 · 2020: 47 · 2019: 65 · 2018: 54 · 2017: 37 · 2016: 40 · 2015: 48 · 2014: 42 · 2013: 48 · 2012: 47
FWCI: 66.01
Type: journal article
Language: en
Open access: closed
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor, 6434 d)
Locations: Cognitive Science (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
Keywords: forward model, internal models, supervised learning, adaptive systems, learned models, multilayer networks, dynamical systems

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

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Forward Models: Supervised Learning with a Distal Teacher

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