scholarly journals Learning logic programs with structured background knowledge☆☆An extended abstract of this paper appeared in: L. De Raedt (Ed.), Proceedings of the Fifth International Workshop on Inductive Logic Programming, Tokyo, Japan, 1995, pp. 53–76, Scientific Report of the Department of Computer Science, Katholieke Universiteit Leuven, and also in the post-conference volume: L. De Raedt (Ed.), Advances in Inductive Logic Programming, IOS Press, Amsterdam/Ohmsha, Tokyo, 1996, pp. 172–191.

2001 ◽  
Vol 128 (1-2) ◽  
pp. 31-97 ◽  
Author(s):  
Tamás Horváth ◽  
György Turán
Keyword(s):  
Logic Programming ◽  
Computer Science ◽  
Inductive Logic Programming ◽  
International Workshop ◽  
Inductive Logic ◽  
Background Knowledge ◽  
Logic Programs ◽  
Scientific Report ◽  
Learning Logic

scholarly journals Turning 30: New Ideas in Inductive Logic Programming

2020 ◽  
Author(s):  
Andrew Cropper ◽  
Sebastijan Dumančić ◽  
Stephen H. Muggleton
Keyword(s):  
Machine Learning ◽  
Logic Programming ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Background Knowledge ◽  
Training Data ◽  
Future Research ◽  
Logic Programs ◽  
Recursive Programs ◽  
New Ideas

Common criticisms of state-of-the-art machine learning include poor generalisation, a lack of interpretability, and a need for large amounts of training data. We survey recent work in inductive logic programming (ILP), a form of machine learning that induces logic programs from data, which has shown promise at addressing these limitations. We focus on new methods for learning recursive programs that generalise from few examples, a shift from using hand-crafted background knowledge to learning background knowledge, and the use of different technologies, notably answer set programming and neural networks. As ILP approaches 30, we also discuss directions for future research.

scholarly journals Forgetting to Learn Logic Programs

2020 ◽  
Vol 34 (04) ◽  
pp. 3676-3683
Author(s):  
Andrew Cropper
Keyword(s):  
Logic Programming ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Background Knowledge ◽  
Experimental Results ◽  
Learning Performance ◽  
Sample Complexity ◽  
Logic Programs ◽  
Hypothesis Space ◽  
Alternative Approaches

Most program induction approaches require predefined, often hand-engineered, background knowledge (BK). To overcome this limitation, we explore methods to automatically acquire BK through multi-task learning. In this approach, a learner adds learned programs to its BK so that they can be reused to help learn other programs. To improve learning performance, we explore the idea of forgetting, where a learner can additionally remove programs from its BK. We consider forgetting in an inductive logic programming (ILP) setting. We show that forgetting can significantly reduce both the size of the hypothesis space and the sample complexity of an ILP learner. We introduce Forgetgol, a multi-task ILP learner which supports forgetting. We experimentally compare Forgetgol against approaches that either remember or forget everything. Our experimental results show that Forgetgol outperforms the alternative approaches when learning from over 10,000 tasks.

scholarly journals Learning Large Logic Programs By Going Beyond Entailment

2020 ◽  
Author(s):  
Andrew Cropper ◽  
Sebastijan Dumančic
Keyword(s):  
Logic Programming ◽  
Loss Function ◽  
Inductive Logic Programming ◽  
State Of The Art ◽  
Inductive Logic ◽  
Program Synthesis ◽  
Loss Functions ◽  
Logic Programs ◽  
Binary Decision ◽  
Best First Search

A major challenge in inductive logic programming (ILP) is learning large programs. We argue that a key limitation of existing systems is that they use entailment to guide the hypothesis search. This approach is limited because entailment is a binary decision: a hypothesis either entails an example or does not, and there is no intermediate position. To address this limitation, we go beyond entailment and use 'example-dependent' loss functions to guide the search, where a hypothesis can partially cover an example. We implement our idea in Brute, a new ILP system which uses best-first search, guided by an example-dependent loss function, to incrementally build programs. Our experiments on three diverse program synthesis domains (robot planning, string transformations, and ASCII art), show that Brute can substantially outperform existing ILP systems, both in terms of predictive accuracies and learning times, and can learn programs 20 times larger than state-of-the-art systems.

scholarly journals Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME

2019 ◽  
Vol 33 ◽  
pp. 3052-3059 ◽  
Author(s):  
Farhad Shakerin ◽  
Gopal Gupta
Keyword(s):  
Logic Programming ◽  
Inductive Logic Programming ◽  
State Of The Art ◽  
Inductive Logic ◽  
Nonmonotonic Logic ◽  
Global Behavior ◽  
Logic Programs ◽  
Tree Models ◽  
Boosted Tree ◽  
Classification Evaluation

We present a heuristic based algorithm to induce nonmonotonic logic programs that will explain the behavior of XGBoost trained classifiers. We use the technique based on the LIME approach to locally select the most important features contributing to the classification decision. Then, in order to explain the model’s global behavior, we propose the LIME-FOLD algorithm —a heuristic-based inductive logic programming (ILP) algorithm capable of learning nonmonotonic logic programs—that we apply to a transformed dataset produced by LIME. Our proposed approach is agnostic to the choice of the ILP algorithm. Our experiments with UCI standard benchmarks suggest a significant improvement in terms of classification evaluation metrics. Meanwhile, the number of induced rules dramatically decreases compared to ALEPH, a state-of-the-art ILP system.

scholarly journals Induction as a Search Procedure

2008 ◽  
pp. 166-216 ◽  
Author(s):  
Stasinos Konstantopoulos ◽  
Rui Camacho ◽  
Nuno A. Fonseca ◽  
Vítor Santos Costa
Keyword(s):  
Logic Programming ◽  
Computer Science ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Search Space ◽  
Search Algorithms ◽  
Stochastic Algorithms ◽  
Search Procedure ◽  
Formal Definition ◽  
Version Spaces

This chapter introduces Inductive Logic Programming (ILP) from the perspective of search algorithms in Computer Science. It first briefly considers the Version Spaces approach to induction, and then focuses on Inductive Logic Programming: from its formal definition and main techniques and strategies, to priors used to restrict the search space and optimized sequential, parallel, and stochastic algorithms. The authors hope that this presentation of the theory and applications of Inductive Logic Programming will help the reader understand the theoretical underpinnings of ILP, and also provide a helpful overview of the State-of-the-Art in the domain.

ILP Applications to Software Engineering

2011 ◽  
pp. 74-102
Author(s):  
Daniele Gunetti
Keyword(s):  
Software Engineering ◽  
Logic Programming ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Logic Programs ◽  
Automatic Synthesis ◽  
Integrated Framework ◽  
Testing And Debugging ◽  
True Logic

Though inductive logic programming (ILP for short) should mean the “induction of logic programs”, most research and applications of this area are only loosely related to logic programming. In fact, the automatic synthesis of “true” logic programs is a difficult task, since it cannot be done without a lot of information on the sought programs, and without the ability to describe in a simple way well-restricted searching spaces. In this chapter, we argue that, if such knowledge is available, inductive logic programming can be used as a valid tool for software engineering, and we propose an integrated framework for the development, maintenance, reuse, testing, and debugging of logic programs.

scholarly journals Least Generalizations and Greatest Specializations of Sets of Clauses

1996 ◽  
Vol 4 ◽  
pp. 341-363 ◽  
Author(s):  
S. H. Nienhuys-Cheng
Keyword(s):  
Logic Programming ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Background Knowledge ◽  
Ordered Sets ◽  
Finite Sets ◽  
Systematic Treatment ◽  
Finite Set ◽  
Complete Discussion ◽  
Survey Results

The main operations in Inductive Logic Programming (ILP) are generalization and specialization, which only make sense in a generality order. In ILP, the three most important generality orders are subsumption, implication and implication relative to background knowledge. The two languages used most often are languages of clauses and languages of only Horn clauses. This gives a total of six different ordered languages. In this paper, we give a systematic treatment of the existence or non-existence of least generalizations and greatest specializations of finite sets of clauses in each of these six ordered sets. We survey results already obtained by others and also contribute some answers of our own. Our main new results are, firstly, the existence of a computable least generalization under implication of every finite set of clauses containing at least one non-tautologous function-free clause (among other, not necessarily function-free clauses). Secondly, we show that such a least generalization need not exist under relative implication, not even if both the set that is to be generalized and the background knowledge are function-free. Thirdly, we give a complete discussion of existence and non-existence of greatest specializations in each of the six ordered languages.

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