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Automated Refinement of First-Order Horn-Clause Domain Theories

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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On the proper definition of minimality in specialization and theory revision
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Foundations of Logic Programming
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Foundations of Logic Programming
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Learning Logical Definitions from Relations
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Interactive concept-learning and constructive induction by analogy
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Extracting Refined Rules from Knowledge-Based Neural Networks
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The Utility of Knowledge in Inductive Learning
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A Framework for Average Case Analysis of Conjunctive Learning Algorithms
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Inductive logic programming
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Knowledge base refinement using apprenticeship learning techniques
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Qualitative simulation
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Automatic knowledge base refinement for classification systems
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Cmpositional modeling: finding the right model for the job
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Theory refinement combining analytical and empirical methods
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Modeling a dynamic environment using a Bayesian multiple hypothesis approach
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Qualitative reasoning: Modeling and simulation with incomplete knowledge
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Prolog programming for artificial intelligence
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Model of Inexact Reasoning in Medicine
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A KNOWLEDGE INTENSIVE APPROACH TO CONCEPT INDUCTION
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Machine Invention of First-order Predicates by Inverting Resolution
inferredcites →https://doi.org/10.1016/b978-0-934613-64-4.50041-4openalex-snapshot
The Interdependencies of Theory Formation, Revision, and Experimentation
inferredcites →https://doi.org/10.1016/b978-1-4832-1447-4.50018-3openalex-snapshot
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The Flexibility of Speculative Refinement
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Learning Qualitative Models of Dynamic Systems
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Determinate Literals in Inductive Logic Programming
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Constraints on Predicate Invention
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Revising Relational Domain Theories
inferredcites →https://doi.org/10.1016/b978-1-55860-200-7.50099-4openalex-snapshot
The DUCTOR: A Theory Revision System for Propositional Domains
inferredcites →https://doi.org/10.1016/b978-1-55860-200-7.50109-4openalex-snapshot
Improving Shared Rules in Multiple Category Domain Theories
inferredcites →https://doi.org/10.1016/b978-1-55860-247-2.50018-8openalex-snapshot
Compiling Prior Knowledge Into an Explicit Bias
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Sub-unification: A Tool for Efficient Induction of Recursive Programs
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Refining a Relational Theory with Multiple Faults in the Concept and Subconcepts
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Interactive Concept-Learning and Constructive Induction by Analogy
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Combining Connectionist and Symbolic Learning to Refine Certainty Factor Rule Bases
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Selection of Offspring Size at Independence and Other Size-Versus-Number Strategies
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Non-monotonic Learning
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Book review: Computational Models of Scientific Discovery and Theory Formation Edited by Jeff Shrager & Pat Langley (Morgan Kaufmann San Mateo, CA, 1990)
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The Interdependencies of Theory Formation, Revision, and Experimentation
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Algorithmic Program Debugging
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A Preliminary PAC Analysis of Theory Revision

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