Inductive logic programming at 30: a new introduction
A Cropper, S Dumančić - Journal of Artificial Intelligence Research, 2022 - jair.org
Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce
a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we …
a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we …
Turning 30: New ideas in inductive logic programming
Common criticisms of state-of-the-art machine learning include poor generalisation, a lack of
interpretability, and a need for large amounts of training data. We survey recent work in …
interpretability, and a need for large amounts of training data. We survey recent work in …
[HTML][HTML] Learning programs by learning from failures
We describe an inductive logic programming (ILP) approach called learning from failures. In
this approach, an ILP system (the learner) decomposes the learning problem into three …
this approach, an ILP system (the learner) decomposes the learning problem into three …
[HTML][HTML] Inductive logic programming at 30
Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to
induce a hypothesis (a logic program) that generalises given training examples and …
induce a hypothesis (a logic program) that generalises given training examples and …
A critical review of inductive logic programming techniques for explainable AI
Despite recent advances in modern machine learning algorithms, the opaqueness of their
underlying mechanisms continues to be an obstacle in adoption. To instill confidence and …
underlying mechanisms continues to be an obstacle in adoption. To instill confidence and …
Provenance-guided synthesis of datalog programs
We propose a new approach to synthesize Datalog programs from input-output
specifications. Our approach leverages query provenance to scale the counterexample …
specifications. Our approach leverages query provenance to scale the counterexample …
Semantic code refactoring for abstract data types
Modifications to the data representation of an abstract data type (ADT) can require
significant semantic refactoring of the code. Motivated by this observation, this paper …
significant semantic refactoring of the code. Motivated by this observation, this paper …
Learning security classifiers with verified global robustness properties
Many recent works have proposed methods to train classifiers with local robustness
properties, which can provably eliminate classes of evasion attacks for most inputs, but not …
properties, which can provably eliminate classes of evasion attacks for most inputs, but not …
Synthesizing datalog programs using numerical relaxation
The problem of learning logical rules from examples arises in diverse fields, including
program synthesis, logic programming, and machine learning. Existing approaches either …
program synthesis, logic programming, and machine learning. Existing approaches either …
Syntax-guided synthesis of datalog programs
Datalog has witnessed promising applications in a variety of domains. We propose a
programming-by-example system, ALPS, to synthesize Datalog programs from input-output …
programming-by-example system, ALPS, to synthesize Datalog programs from input-output …