Efficient instance and hypothesis space revision in Meta-Interpretive Learning

Inductive Logic Programming (ILP) is a form of Machine Learning. The goal of ILP is to induce hypotheses, as logic programs, that generalise training examples. ILP is characterised by a high expressivity, generalisation ability and interpretability. Meta-Interpretive Learning (MIL) is a state-of-the-art sub-field of ILP. However, current MIL approaches have limited efficiency: the sample and learning complexity respectively are polynomial and exponential in the number of clauses. My thesis is that improvements over the sample and learning complexity can be achieved in MIL through instance and hypothesis space revision. Specifically, we investigate 1) methods that revise the instance space, 2) methods that revise the hypothesis space and 3) methods that revise both the instance and the hypothesis spaces for achieving more efficient MIL. First, we introduce a method for building training sets with active learning in Bayesian MIL. Instances are selected maximising the entropy. We demonstrate this method can reduce the sample complexity and supports efficient learning of agent strategies. Second, we introduce a new method for revising the MIL hypothesis space with predicate invention. Our method generates predicates bottom-up from the background knowledge related to the training examples. We demonstrate this method is complete and can reduce the learning and sample complexity. Finally, we introduce a new MIL system called MIGO for learning optimal two-player game strategies. MIGO learns from playing: its training sets are built from the sequence of actions it chooses. Moreover, MIGO revises its hypothesis space with Dependent Learning: it first solves simpler tasks and can reuse any learned solution for solving more complex tasks. We demonstrate MIGO significantly outperforms both classical and deep reinforcement learning. The methods presented in this thesis open exciting perspectives for efficiently learning theories with MIL in a wide range of applications including robotics, modelling of agent strategies and game playing.Open Acces

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

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

Learning Logic Programs by Discovering Higher-Order Abstractions

Discovering novel abstractions is important for human-level AI. We introduce an approach to discover higher-order abstractions, such as map, filter, and fold. We focus on inductive logic programming, which induces logic programs from examples and background knowledge. We introduce the higher-order refactoring problem, where the goal is to compress a logic program by introducing higher-order abstractions. We implement our approach in STEVIE, which formulates the higher-order refactoring problem as a constraint optimisation problem. Our experimental results on multiple domains, including program synthesis and visual reasoning, show that, compared to no refactoring, STEVIE can improve predictive accuracies by 27% and reduce learning times by 47%. We also show that STEVIE can discover abstractions that transfer to different domain

L'obsession de la dualité chez Naïm Kattan

Arrivé au Canada en 1954, Naïm Kattan, un jeune émigré juif d'origine irakienne, a réussi à s'imposer comme l'une des figures importantes de la littérature de son pays d'adoption. Fervent défenseur de la langue française, il n'en demeure pas moins un intellectuel ouvert sur le monde et sur les autres. Son style et le contenu de ses œuvres se caractérisent par une utilisation obsessionnelle, tant dans la forme que dans le fond, de la dualité qui traduit chez cet auteur prolifique une volonté de rapprocher ce que tout semble opposer en apparence.Arrived in Canada in 1954, Naim Kattan, a young Jew who has emigrated from Iraq, has successfully made a name for himself in the literaly field, and became one of the most important literary figures of his adopted country. Even if he is a fervent advocate of the French language, he remains open to the world and to the others. Its style and the content of his works are characterized by an obsessive use, both in form and content, of duality that reflects the desire of this prolific author to bring closer the things which seem to be opposed in appearance

La production de viande de chameau : état des connaissances, situation actuelle et perspectives

Camel meat is a product regularly consumed in arid countries and it is one of the rare products from this species being subject to a regional export market, even international, if the meat from Australian wild camel is included although a poorly evaluated part of this market is out of the official sector. The main exporting countries are located in the Horn of Africa and the Sahelian area whereas the importing countries are the Gulf States and North Africa. Meat productivity in the camel is rather low although the dressing percentages approach those of cattle, especially in animals coming from the more intensive production systems. However, there is a tradition of pastoral fattening having good results. Camel meat is rather close to beef as well in its total chemical composition as in its gustatory characteristics and nutritive value. However, because of fat concentration in the hump, camel muscles give relatively low-fat meat and are particularly low in cholesterol, which can make a good commercial argument.La viande de chameau est un produit régulièrement consommé dans les pays arides et c’est un des rares produits de cette espèce faisant l’objet d’un marché d’export régional, voire international si on inclut la viande de chameaux « marrons » d’Australie, bien qu’une part mal évaluée de ce marché se situe dans le secteur informel. Les principaux pays exportateurs se situent dans la Corne de l’Afrique et dans la région sahélienne alors que les pays importateurs sont surtout les pays du Golfe et d’Afrique du Nord. La productivité en viande chez le chameau est plutôt faible bien que les rendements carcasse se rapprochent de ceux des bovins, surtout chez les animaux provenant des systèmes de production plus intensifs. Il existe toutefois une tradition d’embouche cameline pastorale obtenant de bons résultats. La viande de chameau est assez proche de la viande de boeuf tant dans sa composition chimique globale que dans ses particularités gustatives et sa valeur nutritionnelle. Toutefois, du fait de la concentration du gras dans la bosse, la viande de chameau apparaît relativement maigre et particulièrement pauvre en cholestérol, ce qui peut en faire un argument commercial certain

Chinese Consumers’ Attitudes and Potential Acceptance toward Artificial Meat

The interest for artificial meat has recently expanded. However, from the literature, perception of artificial meat in China is not well known. A survey was thus carried out to investigate Chinese attitudes toward artificial meat. The answers of 4666 respondents concluded that 19.9% and 9.6% of them were definitely willing and unwilling to try artificial meat respectively, whereas 47.2% were not willing to eat it regularly, and 87.2% were willing to pay less for it compared to conventional meat. Finally, 52.9% of them will accept artificial meat as an alternative to conventional meat. Emotional resistance such as the perception of “absurdity or disgusting” would lead to no willingness to eat artificial meat regularly. The main concerns were related to safety and unnaturalness, but less to ethical and environmental issues as in Western countries. Nearly half of the respondents would like artificial meat to be safe, tasty, and nutritional. Whereas these expectations have low effects on willingness to try, they may induce consumers’ rejection to eat artificial meat regularly, underlying the weak relationship between wishes to try and to eat regularly. Thus, potential acceptance of artificial meat in China depends on Chinese catering culture, perception of food and traditional philosophy