Inductive logic programming (ILP) is a symbolic artificial intelligence subfield that employs logic programming to represent instances, background information, and hypotheses. 

Inductive logic programming represents hypotheses and data with first-order logic in machine learning. First-order logic is expressive and declarative. Therefore, inductive logic programming tackles structured data and background knowledge challenges. It is highly advantageous in the fields of bioinformatics and natural language processing.  

Inductive logic programming uses "upgrades" of propositional machine learning systems to solve classification, regression, clustering, and reinforcement learning challenges. It uses logic to represent and justify knowledge. Here, "inductive" indicates philosophy (proposing a theory to explain observed facts) rather than mathematics (proving a quality for all members of a well-ordered set).

Model Inference System

Gordon Plotkin and Ehud Shapiro established the logical inductive machine learning theory. In 1981, Shapiro created the Model Inference System, a Prolog software that inductively inferred logic programs from positive and negative instances. The 1986 Theorist was the first full first-order inductive logic programming implementation. A 1991 work by Stephen Muggleton introduced Inductive Logic Programming. 

Muggleton founded the annual international Inductive Logic Programming conference and introduced Predicate Invention, Inverse resolution, and Inverse entailment. Muggleton initially used inverse entailment in PROGOL. 

Applications

During its early stages, inductive logic programming was utilized to generate logic or functional programs with a broad scope, including data structure manipulation (e.g., sorting or reversing a list). These investigations demonstrated that a limited number of input/output examples could generate compact programs. The recent IT revolution has generated opportunities for additional techniques and applications of this nature in the actual world.

Presently, most computer users are non-programmers who are merely passive consumers of the software supplied. Implementing inductive logic programming can enable these users to automate their daily repetitive duties with computers more efficiently. Inductive logic programming is an extensively utilized technique in education and end-user programming.

Attribute-based learning

Attribute-based learning offers several benefits, including efficiency, relative simplicity, and the availability of effective techniques for managing enormous datasets. Nevertheless, attribute-based learning is constrained to non-relational depictions of entities because the acquired descriptions fail to delineate connections between the entity's components. Thus, attribute-based learning has two significant limitations: background knowledge can only be conveyed in a restricted format, and the concept description language needs to be more suitable for some domains due to the absence of relationships.

Programming

Proficiency in programming languages is one of the requirements for Inductive Logic Programming expert status. You have the option between C++, Python, and Java. 

In artificial intelligence, inductive logic programming generates considerable interest among data scientists worldwide. Programmers interested in a stable career in ILP may enrol in Python courses on Inductive Logic Programming.

Programming using inductive logic is rapidly acquiring prominence as a viable career option. It is possible to advance from solutions engineer or web development programmer positions to project specialist positions over time. Annually, the mean base salary for Project Specialists in the United States can reach $56,509. The number of freelance opportunities for programmers specializing in inductive logic is also increasing.

Career opportunities

The responsibilities of the Solutions Engineer include collecting technical proposals designing and developing complex systems initiatives in collaboration with multiple departments. A Project Specialist supervises and manages Inductive Logic Programming projects by performing engineering-related duties and developing software. Candidates-in-waiting must possess a strong work ethic, desire, ambition, attention to detail, and be exceptionally organized. Additionally, practical communication skills are required for interdepartmental collaboration and correspondence.

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