Learning programs by learning from failures

We describe an inductive logic programming (ILP) approach called learning from failures. In this approach, an ILP system (the learner) decomposes the learning problem into three separate stages: generate, test, and constrain. In the generate stage, the learner generates a hypothesis (a logic program) that satisfies a set of hypothesis constraints (constraints on the syntactic form of hypotheses). In the test stage, the learner tests the hypothesis against training examples. A hypothesis fails when it does not entail all the positive examples or entails a negative example. If a hypothesis fails, then, in the constrain stage, the learner learns constraints from the failed hypothesis to prune the hypothesis space, i.e. to constrain subsequent hypothesis generation. For instance, if a hypothesis is too general (entails a negative example), the constraints prune generalisations of the hypothesis. If a hypothesis is too specific (does not entail all the positive examples), the constraints prune specialisations of the hypothesis. This loop repeats until either (i) the learner finds a hypothesis that entails all the positive and none of the negative examples, or (ii) there are no more hypotheses to test. We introduce Popper, an ILP system that implements this approach by combining answer set programming and Prolog. Popper supports infinite problem domains, reasoning about lists and numbers, learning textually minimal programs, and learning recursive programs. Our experimental results on three domains (toy game problems, robot strategies, and list transformations) show that (i) constraints drastically improve learning performance, and (ii) Popper can outperform existing ILP systems, both in terms of predictive accuracies and learning times.Comment: Accepted for the machine learning journa

Identifying and inferring objects from textual descriptions of scenes from books

Fiction authors rarely provide detailed descriptions of scenes, preferring the reader to fill in the details using their imagination. Therefore, to perform detailed text-to-scene conversion from books, we need to not only identify explicit objects but also infer implicit objects. In this paper, we describe an approach to inferring objects using Wikipedia and WordNet. In our experiments, we are able to infer implicit objects such as monitor and computer by identifying explicit objects such as keyboard

Forgetting to learn logic programs

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.Comment: AAAI2

Inductive logic programming at 30: a new introduction

Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we provide a new introduction to the field. We introduce the necessary logical notation and the main learning settings; describe the building blocks of an ILP system; compare several systems on several dimensions; describe four systems (Aleph, TILDE, ASPAL, and Metagol); highlight key application areas; and, finally, summarise current limitations and directions for future research.Comment: Paper under revie

Relational program synthesis with numerical reasoning

Program synthesis approaches struggle to learn programs with numerical values. An especially difficult problem is learning continuous values over multiple examples, such as intervals. To overcome this limitation, we introduce an inductive logic programming approach which combines relational learning with numerical reasoning. Our approach, which we call NUMSYNTH, uses satisfiability modulo theories solvers to efficiently learn programs with numerical values. Our approach can identify numerical values in linear arithmetic fragments, such as real difference logic, and from infinite domains, such as real numbers or integers. Our experiments on four diverse domains, including game playing and program synthesis, show that our approach can (i) learn programs with numerical values from linear arithmetical reasoning, and (ii) outperform existing approaches in terms of predictive accuracies and learning times

The political economy of health services provision and access in Brazil

The authors examine the impact of local politics and government structure on the allocation of publicly subsidized (SUS) health services across municipios (counties) in Brazil, and on the probability that uninsured individuals who require medical attention actually receive access to those health services. Using data from the 1998 PNAD survey they demonstrate that higher per capita levels of SUS doctors, nurses, and clinic rooms increase the probability that an uninsured individual gains access to health services when he, or she seeks it. The authors find that an increase in income inequality, an increase in the percentage of the population that votes, and an increase in the percentage of votes going to left-leaning candidates are each associated with higher levels of public health services. The per capita provision of doctors, nurses, and clinics is also greater in counties with a popular local leader, and in counties where the county mayor and state governor are politically aligned. Administrative decentralization of health services to the county decreases provision levels, and reduces access to services by the uninsured, unless it is accompanied by good local governance.Health Systems Development&Reform,Health Monitoring&Evaluation,Public Health Promotion,Regional Rural Development,Gender and Health,Health Economics&Finance,Health Monitoring&Evaluation,Health Systems Development&Reform,Regional Rural Development,Gender and Health

Generalisation Through Negation and Predicate Invention

The ability to generalise from a small number of examples is a fundamental challenge in machine learning. To tackle this challenge, we introduce an inductive logic programming (ILP) approach that combines negation and predicate invention. Combining these two features allows an ILP system to generalise better by learning rules with universally quantified body-only variables. We implement our idea in NOPI, which can learn normal logic programs with predicate invention, including Datalog programs with stratified negation. Our experimental results on multiple domains show that our approach can improve predictive accuracies and learning times.Comment: Under peer-revie