Logic-Based Machine Learning

Muggleton, Stephen; Mărginean, Flaviu Adrian

doi:10.1007/978-1-4615-1567-8_14

Cited by 8 publications

(2 citation statements)

References 26 publications

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“…Turing can be seen as one of the first symbolist, as he proposed using a logical representation to build thinking machines (Turing, 1950;Muggleton, 1994a). McCarthy (1959) made the first comprehensive proposal for the use of logic in AI with his advice seeker.…”

Section: A Brief Historymentioning

confidence: 99%

Inductive Logic Programming At 30: A New Introduction

Cropper

Dumančić

2022

jair

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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 provide a new introduction to the field. We introduce the necessary logical notation and the main learning settings; describe the building blocks of an ILP system; compare several systems on several dimensions; describe four systems (Aleph, TILDE, ASPAL, and Metagol); highlight key application areas; and, finally, summarise current limitations and directions for future research.

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Section: A Brief Historymentioning

confidence: 99%

Inductive Logic Programming At 30: A New Introduction

Cropper

Dumančić

2022

jair

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“… 49 Consequently, methods such as logic-based ML make use of inductive logic programming 50 to integrate information and patterns extracted from data with human knowledge in order to make predictions. 51 , 52 …”

Section: Getting Readymentioning

confidence: 99%

Ready, Steady, Go AI: A practical tutorial on fundamentals of artificial intelligence and its applications in phenomics image analysis

Nakhle

Harfouche

2021

Patterns

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Summary High-throughput image-based technologies are now widely used in the rapidly developing field of digital phenomics and are generating ever-increasing amounts and diversity of data. Artificial intelligence (AI) is becoming a game changer in turning the vast seas of data into valuable predictions and insights. However, this requires specialized programming skills and an in-depth understanding of machine learning, deep learning, and ensemble learning algorithms. Here, we attempt to methodically review the usage of different tools, technologies, and services available to the phenomics data community and show how they can be applied to selected problems in explainable AI-based image analysis. This tutorial provides practical and useful resources for novices and experts to harness the potential of the phenomic data in explainable AI-led breeding programs.

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Modelling Metabolic Pathways Using Stochastic Logic Programs-Based Ensemble Methods

Lodhi

Muggleton

2005

Computational Methods in Systems Biology

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Logic-Based Machine Learning

Cited by 8 publications

References 26 publications

Inductive Logic Programming At 30: A New Introduction

Inductive Logic Programming At 30: A New Introduction

Ready, Steady, Go AI: A practical tutorial on fundamentals of artificial intelligence and its applications in phenomics image analysis

Modelling Metabolic Pathways Using Stochastic Logic Programs-Based Ensemble Methods

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