Dynamics of a Classical Conditioning Model
Classical conditioning is a basic learning mechanism in animals and can be found in almost all organisms. If we want to construct robots with abilities matching those of their biological counterparts, this is one of the learning mechanisms that needs to be implemented first. This article describes a...
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Published in | Autonomous robots Vol. 7; no. 1; pp. 41 - 56 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Dordrecht
Springer Nature B.V
01.07.1999
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Subjects | |
Online Access | Get full text |
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Abstract | Classical conditioning is a basic learning mechanism in animals and can be found in almost all organisms. If we want to construct robots with abilities matching those of their biological counterparts, this is one of the learning mechanisms that needs to be implemented first. This article describes a computational model of classical conditioning where the goal of learning is assumed to be the prediction of a temporally discounted reward or punishment based on the current stimulus situation.The model is well suited for robotic implementation as it models a number of classical conditioning paradigms and learning in the model is guaranteed to converge with arbitrarily complex stimulus sequences. This is an essential feature once the step is taken beyond the simple laboratory experiment with two or three stimuli to the real world where no such limitations exist. It is also demonstrated how the model can be included in a more complex system that includes various forms of sensory pre-processing and how it can handle reinforcement learning, timing of responses and function as an adaptive world model. |
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AbstractList | Classical conditioning is a basic learning mechanism in animals and can be found in almost all organisms. If we want to construct robots with abilities matching those of their biological counterparts, this is one of the learning mechanisms that needs to be implemented first. This article describes a computational model of classical conditioning where the goal of learning is assumed to be the prediction of a temporally discounted reward or punishment based on the current stimulus situation. The model is well suited for robotic implementation as it models a number of classical conditioning paradigms and learning in the model is guaranteed to converge with arbitrarily complex stimulus sequences. This is an essential feature once the step is taken beyond the simple laboratory experiment with two or three stimuli to the real world where no such limitations exist. It is also demonstrated how the model can be included in a more complex system that includes various forms of sensory pre-processing and how it can handle reinforcement learning, timing of responses and function as an adaptive world model. |
Author | Balkenius, Christian Morén, Jan |
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CitedBy_id | crossref_primary_10_1007_s00521_016_2314_8 crossref_primary_10_1007_s10339_007_0191_x crossref_primary_10_1016_j_isci_2020_101980 crossref_primary_10_1152_jn_00210_2007 crossref_primary_10_1142_S021952590700115X crossref_primary_10_1371_journal_pcbi_1003466 crossref_primary_10_1371_journal_pone_0111050 crossref_primary_10_1007_s00422_006_0105_5 crossref_primary_10_1007_s00422_017_0730_1 crossref_primary_10_1080_09540091003682553 |
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Copyright | Autonomous Robots is a copyright of Springer, (1999). All Rights Reserved. |
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SubjectTerms | Adaptive systems Autonomous Complex systems Conditioning Learning Machine learning Mathematical models Robots Stimuli |
Title | Dynamics of a Classical Conditioning Model |
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