scholarly journals AUTOMATICALLY EXPLORING HYPOTHESES ABOUT FAULT PREDICTION: A COMPARATIVE STUDY OF INDUCTIVE LOGIC PROGRAMMING METHODS

1999 ◽  
Vol 09 (05) ◽  
pp. 519-546 ◽  
Author(s):  
WILLIAM W. COHEN ◽  
PREMKUMAR T. DEVANBU
Keyword(s):  
Logic Programming ◽  
Inductive Logic Programming ◽  
Description Logics ◽  
Inductive Logic ◽  
Automatic Generation ◽  
Fault Prediction ◽  
Logic Programs ◽  
Large Space ◽  
Fault Density ◽  
Logical Representation

We evaluate a class of learning algorithms known as inductive logic programming (ILP) methods on the task of predicting fault density in C++ classes. Using these methods, a large space of possible hypotheses is searched in an automated fashion; further, the hypotheses are based directly on an abstract logical representation of the software, eliminating the need to manually propose numerical metrics that predict fault density. We compare two ILP systems, FOIL and FLIPPER, and conclude that FLIPPER generally outperforms FOIL on this problem. We analyze the reasons for the differing performance of these two systems, and based on the analysis, propose two extensions to FLIPPER: a user-directed bias towards easy-to-evaluate clauses, and an extension that allows FLIPPER to learn "counting clauses". Counting clauses augment logic programs with a variation of the "number restrictions" used in description logics, and significantly improve performance on this problem when prior knowledge is used. We also evaluate the use of ILP techniques for automatic generation of Boolean indicators and numeric metrics from the calling tree representation.

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.

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 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.

ILPNET repositories on WWW: Inductive Logic Programming systems, datasets and bibliography

1996 ◽  
Vol 9 (4) ◽  
pp. 157-206 ◽  
Author(s):  
Nada Lavrač ◽  
Irene Weber ◽  
Darko Zupanič ◽  
Dimitar Kazakov ◽  
Olga Štěpánková ◽  
...  
Keyword(s):  
Logic Programming ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Programming Systems
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