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

Inductive logic programming at 30

Inductive logic programming (ILP) is a form of logic-based machine learning. The goal of ILP is to induce a hypothesis (a logic program) that generalises given training examples and background knowledge. As ILP turns 30, we survey recent work in the field. In this survey, we focus on (i) new meta-level search methods, (ii) techniques for learning recursive programs that generalise from few examples, (iii) new approaches for predicate invention, and (iv) the use of different technologies, notably answer set programming and neural networks. We conclude by discussing some of the current limitations of ILP and discuss directions for future research.Comment: Extension of IJCAI20 survey paper. arXiv admin note: substantial text overlap with arXiv:2002.11002, arXiv:2008.0791

Logical Reduction of Metarules

International audienceMany forms of inductive logic programming (ILP) use metarules, second-order Horn clauses, to define the structure of learnable programs and thus the hypothesis space. Deciding which metarules to use for a given learning task is a major open problem and is a trade-off between efficiency and expressivity: the hypothesis space grows given more metarules, so we wish to use fewer metarules, but if we use too few metarules then we lose expressivity. In this paper, we study whether fragments of metarules can be logically reduced to minimal finite subsets. We consider two traditional forms of logical reduction: subsumption and entailment. We also consider a new reduction technique called derivation reduction, which is based on SLD-resolution. We compute reduced sets of metarules for fragments relevant to ILP and theoretically show whether these reduced sets are reductions for more general infinite fragments. We experimentally compare learning with reduced sets of metarules on three domains: Michalski trains, string transformations, and game rules. In general, derivation reduced sets of metarules outperform subsumption and entailment reduced sets, both in terms of predictive accuracies and learning times

Judicial decision-making and extra-legal influences: Neurolinguistic Programming as a candidate framework to understand persuasion in the legal context

Jurister försöker påverka rättsliga beslutsprocesser med hjälp av övertalning, men den befintliga litteraturen om övertalning i rättssalen är förvånansvärt begränsad med fokus på enskilda tekniker i isolering; inga omfattande integrerade ramverk finns tillgängliga. Vi föreslår en populär kommersiell metod för övertalning, Neurolingvistisk Programmering (NLP), som startpunkt för att utveckla en modell som kan fylla detta gap. Först presenterar vi en bred analys av rättsliga beslutsprocesser och utomrättsliga faktorer som påverkar dem. Därefter utsätter vi centrala aspekter av NLP för noggrann granskning. Slutligen syntetiserar vi dessa trådar i en mångfacetterad bedömning av NLPs potentiella användbarhet som ett omfattande och integrerat ramverk för att förstå och beskriva juristers övertalningsprocesser i rättssalen. Vi hävdar att NLP kan beskriva dessa beteenden och strategier både genom en självreflexiv logik, som ett resultat av dess breda inflytande, men också för mer generella övertalningsprocesser tack vare ett stort antal överensstämmelser mellan NLP-begrepp och resultat från vetenskaplig litteratur. Även om dessa överensstämmelser är ytliga, tyder det faktum att NLP integrerar sina förenklade koncept i ett sammanhållet ramverk, som spänner argumentations- och presentations-dimensioner för övertalning, att det förhållandevis enkelt kan anpassas till en praktisk modell för att beskriva och förstå övertalning i rättssalen. Vidare forskning är indikerad.Trial advocates seek to influence the outcomes of judicial decision-making processes using persuasion, but the existing literature regarding persuasion in the courtroom is surprisingly piecemeal, focusing on individual techniques in isolation; no comprehensive frameworks for integrating these techniques, or for systematically analyzing advocates’ attempts to enact persuasion in the courtroom, have been developed. We propose a popular commercial technology for persuasion, Neurolinguistic Programming (NLP), as a candidate framework that might be modified and adapted to fill this gap. First we present a wide-ranging, discursive analysis of judicial decision-making processes and extra-legal factors that influence them. Next, core aspects of NLP theory are subjected to careful examination. Finally, these threads are synthesized into a multifaceted assessment of NLP’s potential utility as a comprehensive and integrative framework for understanding and describing how litigators enact persuasion in the courtroom. We argue that NLP can describe these behaviors and strategies both by way of a self-reflexive logic resulting from its popular influence, but also as a more general, context independent model by virtue of a large number of correspondences between NLP concepts and findings from the scholarly literature. Although these correspondences are superficial, the fact that NLP integrates its simplified, folk concepts into a coherent framework spanning argumentative and presentational dimensions of persuasion suggests that it might readily be adapted into a useful descriptive model for understanding persuasion in the courtroom. Further scholarly attention is indicated

Learning programs with magic values

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

Cloud computing and context-awareness: A study of the adapted user experience

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Today, mobile technology is part of everyday life and activities and the mobile ecosystems are blossoming, with smartphones and tablets being the major growth drivers. The mobile phones are no longer just another device, we rely on their capabilities in work and in private. We look to our mobile phones for timely and updated information and we rely on this being provided any time of any day at any place. Nevertheless, no matter how much you trust and love your mobile phone the quality of the information and the user experience is directly associated with the sources and presentation of information. In this perspective, our activities, interactions and preferences help shape the quality of service, content and products we use. Context-aware systems use such information about end-users as input mechanisms for producing applications based on mobile, location, social, cloud and customized content services. This represents new possibilities for extracting aggregated user-centric information and includes novel sources for context-aware applications. Accordingly, a Design Research based approach has been taken to further investigate the creation, presentation and tailoring of user-centric information. Through user evaluated experiments findings show how multi-dimensional context-aware information can be used to create adaptive solutions tailoring the user experience to the users’ needs. Research findings in this work; highlight possible architectures for integration of cloud computing services in a heterogeneous mobile environment in future context-aware solutions. When it comes to combining context-aware results from local computations with those of cloud based services, the results provide findings that give users tailored and adapted experiences based on the collective efforts of the two

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