Inductive logic programming (ILP) is a subfield of symbolic artificial intelligence which uses logic programming as a uniform representation for examples, background knowledge and hypotheses.

Apr 28, 2022 · Inductive Logic Programming (ILP) is a sub territory of AI which deals with the induction of hypothesized predicate definitions from examples and background knowledge. …

May 28, 2024 · Inductive reasoning, a fundamental aspect of human logic and reasoning, plays a pivotal role in the realm of artificial intelligence (AI). This cognitive process involves making …

Aug 18, 2020 · 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 …

May 2, 2025 · Inductive Logic Programming (ILP) is a subfield of artificial intelligence that combines the strengths of logic programming and machine learning to enable computers to …

Inductive Logic Programming (ILP) is a rule-based learning method that seeks underlying patterns in data by deriving a set of if-then logic rules. These rules describe positive instances but not …

Inductive Logic Programming is a powerful and flexible approach to learning from structured data, that can handle complex and diverse domains without requiring feature engineering or …

Inductive logic programming (ILP), a subfield of symbolic AI, plays a promising role in generating interpretable explanations because of its intuitive logic-driven framework. ILP effectively …

Inductive logic programming (ILP) studies the learning of (Prolog) logic programs and other relational knowledge from examples. Most machine learning algorithms are restricted to finite, …

Goal of an ILP system is to find a set of hypothesis that: Explains (covers) the positive examples – Completeness. Are consistent with the negative examples – Consistency. Generalising a …