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A subfield of symbolic artificial intelligence known as inductive logic programming (ILP) use logic programming as a consistent representation for examples, background knowledge, and hypotheses. An ILP system will develop a hypothesised logic program in the event that it is provided with an encoding of the known background knowledge and a collection of examples that are represented as a logical database of facts. This program will involve all of the positive examples and none of the negative instances.In this model, the hypothesis is derived from positive instances, negative examples, and background knowledge.
How You Will Benefit
(I) Insights, and validations about the following topics:
Chapter 1: Inductive Logic Programming
Chapter 2: Stephen Muggleton
Chapter 3: Progol
Chapter 4: Program Synthesis
Chapter 5: Inductive Programming
Chapter 6: First-Order Logic
Chapter 7: List of Rules of Inference
Chapter 8: Disjunctive Normal Form
Chapter 9: Resolution (Logic)
Chapter 10: Answer Set Programming
(II) Answering the public top questions about inductive logic programming.
(III) Real world examples for the usage of inductive logic programming in many fields.
(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of inductive logic programming' technologies.
Who This Book Is For
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of inductive logic programming.
