A Note on Inductive Generalization

By Gordon D. Plotkin. 2008

608 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/aeb91d9afd98334eece191b8d3ce3239d4f91d71

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Citations in the corpus

Statistical Relational Artificial Intelligence: Logic, Probability, and Computation (2016, background): “Plotkin [1970] studied the quotient set induced by -subsumption and proven that it is a complete lattice; see also De Raedt [2008, 2010] for more details.”
Neural, symbolic and neural-symbolic reasoning on knowledge graphs (2021, methodology): “Two kinds of approaches are usually used to derive rules, where the top-down approach begins from general rules and adds new atoms to improve the coverage precision of positive instances (Clark and Niblett, Fig.”
A Survey on Neural Network Interpretability (2020, methodology): “After obtaining the propositional rules by the above methods, Gyan uses the Least General Generalization (LGG [97]) method to generate first-order logic rules from them.”
Inductive Logic Programming (2014, methodology): “Systems like XHail [17] and Imparo [11] follow a specific to general search, and are called bottom-up approaches, whereas systems like HYPER [2], TopLog [15] and Metagol [16] are top-down approaches as they follow a general to specific type of search for the computation of inductive hypotheses.”
Inductive Programming: A Survey of Program Synthesis Techniques (2009, methodology): “Furthermore, Summers’ ad-hoc method to introduce additional variables is systematized by computing least general generalization (lgg) [9] of successive fragments.”
Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach (2006, background): “The maximal pattern of a set of terms can be calculated by first order anti-unification (Plotkin, 1969).”