Inductive Logic Programming At 30: A New Introduction

Efficient omission of symmetric solution candidates is essential for combinatorial problem-solving. Most of the existing approaches are instance-specific and focus on the automatic computation of Symmetry Breaking Constraints (SBCs) for each given problem instance. However, the application of such approaches to large-scale instances or advanced problem encodings might be problematic since the computed SBCs are propositional and, therefore, can neither be meaningfully interpreted nor transferred to other instances. As a result, a time-consuming recomputation of SBCs must be done before every invocation of a solver. To overcome these limitations, we introduce a new model-oriented approach for Answer Set Programming that lifts the SBCs of small problem instances into a set of interpretable first-order constraints using the Inductive Logic Programming paradigm. Experiments demonstrate the ability of our framework to learn general constraints from instance-specific SBCs for a collection of combinatorial problems. The obtained results indicate that our approach significantly outperforms a state-of-the-art instance-specific method as well as the direct application of a solver.

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“…bias may exclude solutions from the search space, thus resulting in suboptimal SBCs (Cropper & Dumančć, 2020).…”

Section: Inductive Logic Programmingmentioning

confidence: 99%

Lifting symmetry breaking constraints with inductive logic programming

2022

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“…Although fundamental to AI, learning programs with magic values is difficult for existing program synthesis approaches. For instance, many recent inductive logic programming (ILP) [1,2] approaches first enumerate all possible rules allowed in a program [3][4][5][6] and then search for a subset of them. For example, ASPAL [3] precomputes every possible rule and uses an answer set solver to find a subset of them.…”

Section: Positive Examplesmentioning

confidence: 99%

Learning programs with magic values

Hocquette¹,

Cropper²

2022

Preprint

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A magic value in a program is a constant symbol that is essential for the execution of the program but has no clear explanation for its choice. Learning programs with magic values is difficult for existing program synthesis approaches. To overcome this limitation, we introduce an inductive logic programming approach to efficiently learn programs with magic values. Our experiments on diverse domains, including program synthesis, drug design, and game playing, show that our approach can (i) outperform existing approaches in terms of predictive accuracies and learning times, (ii) learn magic values from infinite domains, such as the value of pi, and (iii) scale to domains with millions of constant symbols.

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“…3. A hypothesis language of function free predicates L, and a refinement operator ρ under θ-subsumption described in Plotkin (1971) (for more details, see the paper by Cropper and Dumancic (2020)). The hypothesis language L is also assumed to be stratified.…”

Section: Inductive Logic Programmingmentioning

confidence: 99%

“…ILP provides an excellent solution for XAI. ILP is a thriving field and a large number of such clause search algorithms have been devised as described by Muggleton et al (2012) and Cropper and Dumancic (2020). The search in these ILP algorithms is performed either top down or bottom up.…”

Section: Introductionmentioning

confidence: 99%

FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data

Wang¹,

Shakerin²,

Gupta³

2022

Theory and Practice of Logic Programming

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FOLD-RM is an automated inductive learning algorithm for learning default rules for mixed (numerical and categorical) data. It generates an (explainable) answer set programming (ASP) rule set for multi-category classification tasks while maintaining efficiency and scalability. The FOLD-RM algorithm is competitive in performance with the widely used, state-of-the-art algorithms such as XGBoost and multi-layer perceptrons, however, unlike these algorithms, the FOLD-RM algorithm produces an explainable model. FOLD-RM outperforms XGBoost on some datasets, particularly large ones. FOLD-RM also provides human-friendly explanations for predictions.

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Inductive Logic Programming At 30: A New Introduction

Cited by 31 publications

References 219 publications

Lifting symmetry breaking constraints with inductive logic programming

Lifting symmetry breaking constraints with inductive logic programming

Learning programs with magic values

FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data

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