Results 11 to 20 of about 120 (110)
Conflict-Driven Inductive Logic Programming [PDF]
Theory and Practice of Logic Programming, 2022 AbstractThe 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). Learning such expressive programs widens the applicability of ILP considerably; for example, enabling preference ...
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Learning and reasoning with graph data
Frontiers in Artificial Intelligence, 2023 Reasoning about graphs, and learning from graph data is a field of artificial intelligence that has recently received much attention in the machine learning areas of graph representation learning and graph neural networks.
Manfred Jaeger
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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.
Comini M, GORI, ROBERTA, Levi G.
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Induction of constraint logic programs [PDF]
, 1998 Inductive Logic Programming (ILP) is concerned with learning hypotheses from examples, where both examples and hypotheses are represented in the Logic Programming (LP) language. The application of ILP to problems involving numerical information has shown the need for basic numerical background knowledge (e.g. relation “less than”).
Michèle Sebag +2 more
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A Curry-Howard Correspondence for Linear, Reversible Computation [PDF]
Logical Methods in Computer ScienceIn this paper, we present a linear and reversible programming language with inductives types and recursion. The semantics of the languages is based on pattern-matching; we show how ensuring syntactical exhaustivity and non-overlapping of clauses is ...
Kostia Chardonnet +2 more
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Answer Set Programming for Regular Inference
Applied Sciences, 2020 We propose an approach to non-deterministic finite automaton (NFA) inductive synthesis that is based on answer set programming (ASP) solvers. To that end, we explain how an NFA and its response to input samples can be encoded as rules in a logic program.
Wojciech Wieczorek +2 more
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Logical Rule-Based Knowledge Graph Reasoning: A Comprehensive Survey
Mathematics, 2023 With its powerful expressive capability and intuitive presentation, the knowledge graph has emerged as one of the primary forms of knowledge representation and management.
Zefan Zeng, Qing Cheng, Yuehang Si
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Logic programming revisited [PDF]
ACM Transactions on Computational Logic, 2001 Logic programming has been introduced as programming in the Horn clause subset of first-order logic. This view breaks down for the negation as failure inference rule. To overcome the problem, one line of research has been to view a logic program as a set of iff-definitions.
Denecker, Marc +2 more
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BMC Bioinformatics, 2012 Background There is a need for automated methods to learn general features of the interactions of a ligand class with its diverse set of protein receptors. An appropriate machine learning approach is Inductive Logic Programming (ILP), which automatically
A Santos Jose C +4 more
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Preprocessing in Inductive Logic Programming
CoRR, 2021 Inductive logic programming is a type of machine learning in which logic programs are learned from examples. This learning typically occurs relative to some background knowledge provided as a logic program. This dissertation introduces bottom preprocessing, a method for generating initial constraints on the programs an ILP system must consider.
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