Logic programs as specifications in the inductive verification of logic programs
Electronic Notes in Theoretical Computer Science, 2001 AbstractIn this paper we define a new verification method based on an assertion language able to express properties defined by the user through a logic program. We first apply the verification framework defined in [3] to derive sufficient inductive conditions to prove partial correctness.
Marco Comini, Roberta Gori, Giorgio Levi
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A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models [PDF]
BMC Bioinformatics, 2011 Background Remote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequence alignments (MSA), including all positions.
Carbone Alessandra +2 more
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Inductive Logic Programming [PDF]
Lecture Notes in Computer Science, 2011 Inductive logic programming is the subfield of machine learning that uses first order logic to represent hypotheses and data. Because first order logic is expressive and declarative, inductive logic programming specfically targets problems involving structured data and background knowledge. Inductive logic programming tackles a wide variety of problems
水生 久木田
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Best-effort inductive logic programming via fine-grained cost-based hypothesis generation [PDF]
Machine-mediated learning, 2017 We describe the Inspire system which participated in the first competition on inductive logic programming (ILP). Inspire is based on answer set programming (ASP).
P. Schüller, Mishal Benz
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Conflict-driven Inductive Logic Programming [PDF]
Theory and Practice of Logic Programming, 2020 The goal of inductive logic programming (ILP) is to learn a program that explains a set of examples. Until recently, most research on ILP targeted learning Prolog programs. The ILASP system instead learns answer set programs (ASP).
Mark Law
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Inductive Logic Programming in Clementine [PDF]
, 2000 This paper describes the integration of ILP with Clementine. Background on ILP and Clementine is provided, with a description of Clementine's target users. The benefits of ILP to data mining are outlined, and ILP is compared with pre-existing data mining algorithms. Issues of integration between ILP and Clementine are discussed.
Sam Brewer, Tom Khabaza
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Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach [PDF]
Journal of Artificial Intelligence ResearchPartially Observable Markov Decision Processes (POMDPs) are a powerful framework for planning under uncertainty. They allow to model state uncertainty as a belief probability distribution.
Daniele Meli +2 more
semanticscholar +2 more sources
Implementation and evaluation of a communication coaching program: a CFIR-Informed qualitative analysis mapped onto a logic model [PDF]
BMC Medical EducationBackground Coaching programs in graduate medical education have the potential to impact trainee development across multiple core competencies but require rigorous program evaluation to ensure effectiveness.
Rachel M. Jensen +7 more
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Knowledge discovery for pancreatic cancer using inductive logic programming. [PDF]
IET Syst Biol, 2014 Qiu Y +6 more
europepmc +2 more sources
Discovering rules for protein-ligand specificity using support vector inductive logic programming. [PDF]
Protein Eng Des Sel, 2009 Kelley LA +3 more
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