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CSM: A Computational Model of Cumulative Learning
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-662-12405-5_5openalex-snapshot
Learning by Analogy: Formulating and Generalizing Plans from Past Experience
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Classifier Systems and the Animat Problem
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Credit Assignment in Rule Discovery Systems Based on Genetic Algorithms
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Chunking in Soar: The anatomy of a general learning mechanism
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The psychology of animal learning
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LEARNING BY ANALOGY: FORMULATING AND GENERALIZING PLANS FROM PAST EXPERIENCE
inferredcites →https://doi.org/10.1016/b978-0-12-737550-2.50020-8openalex-snapshot
COGNITIVE SYSTEMS BASED ON ADAPTIVE ALGORITHMS11Research reported in this paper was supported in part by the National Science Foundation under grant DCR 71-01997 and by the Horace H. Rackham School of Graduate Studies under grant 387156.
inferredcites →https://doi.org/10.1023/a:1022639103969openalex-snapshot
Chunking in Soar: The Anatomy of a General Learning Mechanism
inferredcites →https://doi.org/10.1609/aimag.v4i4.411openalex-snapshot
Artificial Intelligence Prepares for 2001
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An Analysis Of The Behavior Of A Class Of Genetic Adaptive Systems.
inferredcites →https://doi.org/10.7551/mitpress/1090.001.0001openalex-snapshot
Adaptation in Natural and Artificial Systems
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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A Bigger Learning Classifier Systems Bibliography
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Adaptive expert systems and analogical problem solving
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inferred← citeshttps://doi.org/10.1007/978-3-7091-7533-0_90openalex-snapshot
Classifier System in Traffic Management
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A Soft-Computing Distributed Artificial Intelligence Architecture for Intelligent Buildings
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An Approach to Anytime Learning
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Enhancing Transfer in Reinforcement Learning by Building Stochastic Models of Robot Actions
inferred← citeshttps://doi.org/10.1016/j.neunet.2017.09.001openalex-snapshot
Lifelong learning of human actions with deep neural network self-organization
inferred← citeshttps://doi.org/10.1016/s0020-0255(02)00368-7openalex-snapshot
A hierarchical fuzzy–genetic multi-agent architecture for intelligent buildings online learning, adaptation and control
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Learning and adaptation of an intelligent mobile robot navigator operating in unstructured environment based on a novel online Fuzzy–Genetic system
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Cumulative learning techniques in production rules with fuzzy hierarchy (PRFH) system
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From Genetic Evolution to Engineering Optimization
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Genetics-based machine learning and behavior-based robotics: a new synthesis
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Control and Learning of Ambience by an Intelligent Building
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Conceptual learning and classifier systems with long-term memory
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Genetic and Non-Genetic Operators in ALECSYS
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Evolutionary Algorithms for Reinforcement Learning
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Genetic learner: Discretization and fuzzification of numerical attributes
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Acquiring Knowledge in Extended Hierarchical Censored Production Rules (EHCPRS) System
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