What is Inductive Logic Programming: Definition and Applications

by | May 31, 2018 | Data Analytics

8 Min Read. |

Inductive Logic Programming (ILP), is a subfield of machine learning that learns computer programs from data, where the programs and data are logic programs. It may also be explained as a form of supervised machine learning which uses logic programming (primarily Prolog) as a uniform representation for background knowledge, examples, and induced theories. ILP is preferred over other machine learning approaches because of its easy comprehensibility, intelligibility, and ability to include additional information in the learning problem.

This discussion examines the main premises of ILP, its tools and techniques, theories, applications, and its relation with Artificial Intelligence and Machine Learning.

Through this blog, I aim to cover the following:

  • Inductive Logic Programming Definition: What Is It?
  • Techniques and Applications in Inductive Logic Programming
  • Inductive Logic Programming and Artificial Intelligence
  • Inductive Logic Programming tutorials (that include course details on Artificial Intelligence and Machine Learning) and guidelines for finding one ta suits your career preferences
 Inductive Logic Programming

technological background with computer

Inductive Logic Programming Definition

Inductive Logic Programming (ILP) may be defined as a new discipline that investigates the inductive construction of first-order clausal theories from examples and background knowledge. ILP, as a term, lies at the confluence of machine learning or data mining and logic programming. On the one hand, Inductive Logic Programming aims at finding patterns in data, patterns that can be used to build predictive models or to gain insight in the data, and on the other hand, it investigates the inductive construction of first-order clausal theories from examples and background knowledge.

Inductive Logic Programming is also intricately linked to logic programming because it shares the use of clausal first-order logic as a representation language for both data and hypotheses. The ILP theory is based on proof theory and model theory for the first order predicate calculus. Inductive hypothesis formation is characterized by techniques including inverse resolution, relative least general generalizations, inverse implication, and inverse entailment.

Inductive Logic Programming

inductive logic programming in artificial intelligence

Inductive Logic Programming and Artificial Intelligence

Inductive Logic Programming (ILP) is a sub territory of AI which deals with the induction of hypothesized predicate definitions from examples and background knowledge. Logic programs are treated as a single representation, for example, background knowledge, and hypotheses. ILP is differentiated from the other forms of machine learning both by its use of an expressive representation language and its ability to make use of logically encoded background knowledge.

Molecular biology and natural language are two significant areas of successful applications of ILP. Both the fields have rich sources of background knowledge and benefit from the use of an expressive concept representation languages. For instance, the ILP system Progol has recently been used to generate comprehensible descriptions of the 23 most populated fold classes of proteins, where no such descriptions had previously been formulated manually.

In the natural language area, ILP has not demonstrated higher accuracies than various other machine learning approaches in learning the past tense of English but also better capabilities of learning accurate grammar which translate sentences into deductive database queries. Of late Learning Language in Logic (LLL) is producing several challenges to existing ILP theory and implementations.

Language applications of ILP, however, would require revision and extension of a hierarchically defined set of predicates in which the examples are typically only provided for predicates at the top of the hierarchy. New predicates often need to be invented, and complex recursion is usually involved. Advances in ILP theory and implementation related to the challenges of LLL are already producing beneficial advances in other sequence-oriented applications of ILP.

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Inductive Logic Programming Techniques and Applications

In its earlier days, ILP was applied to synthesizing functional or logic programs for general purpose tasks such as manipulating data structures (for example, sorting or reversing a list). These investigations showed that small programs could be synthesized from a few input/output examples. The recent IT revolution has created real-world opportunities for more such techniques and applications.

Most computer users, today, are non-programmers and are limited to being passive consumers of the software that is made available to them. ILP can empower such users to more effectively leverage computers for automating their daily repetitive tasks. Inductive Logic Programming is widely used in the areas of End-user Programming and Education.

The advantages of attribute-based learning are relative simplicity, efficiency, and the existence of effective techniques for handling humongous data. However, attribute-based learning is limited to non-relational descriptions of objects in the sense that the learned descriptions do not specify relations among the objects’ parts. Attribute-based learning thus has two strong limitations: the background knowledge can be expressed in the rather limited form, and the lack of relations makes the concept description language inappropriate for some domains.

  • End-User Programming:

Computer users often need to create small (and perhaps one-off) scripts to automate repetitive tasks. ILP has proved to be very helpful for these users. For instance, consider the domain of data manipulation. Documents of various types, such as text/log files, spreadsheets, and web pages, offer their creators great flexibility in storing and organizing hierarchical data by combining presentation and formatting with the underlying data model.

However, this makes it extremely difficult for the user(s) to extract the underlying data for common tasks such as data processing, querying, altering the presentation view, or transforming data to another storage format. Existing programmatic solutions to manipulating data (such as Excel macro language, regular expression libraries inside Perl/Python, and JQuery library for Javascript) have a few limitations.

First, the solutions are domain-specific and require prior knowledge or expertise. Second, the user needs to have a thorough understanding of the entire underlying document structure including the data fields. As a result, most users resort to manual copying and pasting, which is a time consuming and erroneous process.

Inductive synthesis can help you with a variety of data manipulation techniques: Extracting data from semi-structured documents including text files, web pages, and spreadsheets; the transformation of atomic data types such as strings or numbers; the transformation of composite data types such as tables and XML; Formatting data. Combining these technologies in a pipeline of extraction, transformation, and formatting can allow end-users to perform sophisticated data manipulation tasks.

  • Computer-aided Education:

Human learning and communication are often structured around examples — it might be a student trying to understand a concept or a trainer finding ways to help the student, clearing his misconceptions, or providing constructive feedback on his paper.

ILP example in computer-based training is useful for all kinds of students.  Example-based reasoning techniques developed in the inductive synthesis community can help automate several repetitive and structured tasks in education including problem generation, solution generation, and feedback generation. These tasks can be automated for a wide variety of STEM subject domains including logic, automata theory, programming, arithmetic, algebra, and geometry.

Other ILP techniques and applications include learning of structure-activity rules for drug design, finite-element mesh analysis design rules, primary-secondary prediction of protein structure and fault diagnosis rules for satellites. ILP, though a broad topic, has gained popularity among researchers for implementations of ILP systems like Aleph and Progol, optimal search theory or hyper-parameter optimization.

Learning from Inductive Logic Programming Example

Being proficient in programming languages is one of the prerequisites for becoming an expert in Inductive Logic Programming. You may choose from C++, Java, or Python. Inductive Logic Programming in Artificial Intelligence is creating ripples among data scientists across the world.  Programmers looking for a steady career in ILP may sign up for Inductive Logic Programming Python courses.

Suggested read Top 10 Python Libraries for Data Science.

Knowing standard data structures STL or Collections is a mandate. Learning by example is the best for gaining a thorough understanding of ILP. You may also practice competitive programming on online judges or look for worked-out examples by experts. Be a part of tech communities like Quora where other programmers discuss issues or problems in ILP. You may also ask questions or post replies to problems. Learning through Inductive Logic Programming examples is always good as it gives you real-life scenarios for practice.

Inductive Logic Programming Career

Inductive logic programming is fast gaining importance as a career option.  You may start working as a solutions engineer or web development programmer, and later move on to Project Specialist roles. The average base salary for Project Specialists goes up to $56,509 per year in the US. Freelancer opportunities for Inductive logic programmers are also growing in number.

The Solutions Engineer role involves collaborating with various departments gathering technical proposals, design, and development of complex systems projects. A Project Specialist working on an Inductive Logic Programming project oversees and manages projects through development and other engineering-related tasks. Aspiring candidates need to be highly organized with drive, ambition, attention to detail, and a strong work ethic. One must also have effective communication skills for interdepartmental collaboration and communication.

Inductive Logic Programming Tutorial

If you are looking for a long and fulfilling career in Inductive Logic Programming, then the time is ripe. The world of data science is opening new opportunities for programmers, and Inductive Logic Programming has already gained momentum both among programmers and researchers.

 Inductive Logic Programming

Inductive Logic Programming Tutorial

Ideally, most recruiters look for candidates with a master’s degree in Electrical Engineering, Computer Science, or Industrial Engineering. Proficiency in Microsoft Office Applications (like Word, Excel, Access, PowerPoint, Outlook, Projects, and Visio) and Oracle Business Systems, and Improvement Analysis Applications is an added advantage.

In addition to the formal degree, one needs to stay abreast of the trends and changes in Inductive Logic Programming. You can enroll for inductive logic programming tutorial for gaining more insights on Applications of ILP to natural language processing or open issues in ILP. A good number of online resources are available for online study or download. You may subscribe to online journals for more information on ILP theories, methodologies, and applications. You may also download inductive logic programming pdf documents for reference.

Inductive Logic Programming, which is a subfield of machine learning, is also closely related to Data Analytics. So, if you are a programmer looking forward to a career change, go for a Data Analytics course for more lucrative career options in ILP. Digital Vidya offers advanced courses in Data Science. Industry-relevant curriculum, pragmatic market-ready approach, hands-on Capstone Project are some of the best reasons for choosing Digital Vidya.

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