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

Late Breaking Papers of the 27th International Conference on Inductive Logic Programming, Orléans, France, September 4-6, 2017

International audienc

Behavioural Preorders on Stochastic Systems - Logical, Topological, and Computational Aspects

Computer systems can be found everywhere: in space, in our homes, in our cars, in our pockets, and sometimes even in our own bodies. For concerns of safety, economy, and convenience, it is important that such systems work correctly. However, it is a notoriously difficult task to ensure that the software running on computers behaves correctly. One approach to ease this task is that of model checking, where a model of the system is made using some mathematical formalism. Requirements expressed in a formal language can then be verified against the model in order to give guarantees that the model satisfies the requirements. For many computer systems, time is an important factor. As such, we need our formalisms and requirement languages to be able to incorporate real time. We therefore develop formalisms and algorithms that allow us to compare and express properties about real-time systems. We first introduce a logical formalism for reasoning about upper and lower bounds on time, and study the properties of this formalism, including axiomatisation and algorithms for checking when a formula is satisfied. We then consider the question of when a system is faster than another system. We show that this is a difficult question which can not be answered in general, but we identify special cases where this question can be answered. We also show that under this notion of faster-than, a local increase in speed may lead to a global decrease in speed, and we take step towards avoiding this. Finally, we consider how to compare the real-time behaviour of systems not just qualitatively, but also quantitatively. Thus, we are interested in knowing how much one system is faster or slower than another system. This is done by introducing a distance between systems. We show how to compute this distance and that it behaves well with respect to certain properties.Comment: PhD dissertation from Aalborg Universit

Lessons learned from eco-district pilot projects : the importance of stakeholder relations

Le changement climatique devenant de plus en plus une réalité à laquelle les villes du monde entier sont confrontées, les menaces environnementales globales et locales à celles-ci soulignent la nécessité d'un nouveau paradigme dans les disciplines du cadre bâti, un nouveau paradigme autant en termes des processus de conception et de planification que des résultats construits. Afin de répondre à cet appel en faveur d'un développement urbain plus durable et plus résilient, au cours de la dernière décennie, un nombre croissant d’outils de quartiers durables ont vu le jour. Alors que plusieurs études ont cherché à comprendre le contenu, la forme et la structure des outils de quartiers durables, il existe encore des lacunes importantes concernant le « comment » de ces outils : Comment les outils de quartiers durables mènent-ils à de meilleures pratiques dans les domaines de l’architecture et de l’urbanisme? Comment ces outils sont-ils mis en oeuvre et quelles leçons peut-on en tirer ? Comment contribuent-ils à briser les silos professionnels pour favoriser une réflexion plus intégrée et collaborative ? Cette thèse vise à expliquer comment les outils de quartiers durables sont utilisés dans la pratique et dans quelle mesure ils contribuent à l'évolution vers un paradigme plus régénératif et résilient. Ce projet de recherche de nature qualitative repose sur trois études de cas approfondies de projets pilotes d'éco-quartiers qui ont expérimenté avec des outils de quartiers durables : d'abord, les projets pilotes « EcoDistricts » à Portland en Oregon ; deuxièmement, le projet pilote « BREEAM-Communities » à Malmö en Suède ; et troisièmement, le projet pilote « superîlot » et l’outil « Urbanisme Écologique » à Barcelone, en Espagne. Le projet de recherche utilise une stratégie exploratoire dans laquelle des entrevues semi-structurées avec les parties prenantes impliquées dans ces projets pilotes permettent de construire un cadre théorique fondé sur le processus. Les résultats démontrent les nombreuses façons dont les outils de quartiers durables ont un impact sur la pratique, en particulier à l'égard des quatre volets suivantes : la collaboration, la participation citoyenne, les relations entre les parties prenantes et la communication visuelle. Les résultats de l'étude de cas ont également mis en lumière les écarts entre les attentes des outils de quartiers durables et la réalité de leur mise en oeuvre. En effet, les études de cas identifient des obstacles externes et internes qui expliquent la raison pour laquelle le nouveau paradigme se manifeste de façon marginale, par l'entremise des outils. Les résultats servent à rappeler que les outils de quartiers durables ne peuvent être compris en vase clos. Au contraire, comment et quand ils sont utilisés et comment ils sont encadrés sont tout aussi importants. L’application réussie de ces outils dépend de diverses conditions qui peuvent conduire à une meilleure collaboration, participation citoyenne, relations entre les parties prenantes et communication visuelle. Celles-ci incluent, par exemple, l’alignement des atouts, des actions et des attentes des parties prenantes ; favoriser les attributs internes dans les équipes de planification, tels que la capacité renforcée, le leadership fort et la confiance dans le processus ; disposer de mécanismes de résolution des conflits ; et en veillant à ce que la participation de la communauté soit non seulement profonde mais suffisamment précoce pour avoir un impact sur la prise de décision. Sur la base de ces résultats, le projet propose un « cadre de soutien aux parties prenantes » pour les outils de quartiers durables de troisième génération. Au niveau macro, il propose trois principes directeurs pour les outils de quartiers durables. À une échelle plus fine, il fournit une série de concepts et de boîtes à outils pour aider les dirigeants d'éco-districts à développer des processus plus régénératifs, résilients et justes.As climate change becomes more and more a reality that cities around the world face, global and local environmental threats to cities highlight the need for a new paradigm in built environment disciplines, a new paradigm as much in terms of design and planning processes as built outcomes. In order to help answer this call for more sustainable and resilient urban development, over the past decade or so, a rapidly increasing number of neighbourhood sustainability frameworks have emerged. While several studies have aimed at understanding the content, form and structure of neighbourhood sustainability frameworks, important knowledge gaps exist concerning the ‘hows’ of these frameworks: How are neighbourhood sustainability frameworks leading to better practice? How are these frameworks being implemented, and what can be learned from this? How are they contributing to breaking down professional silos to foster integrated and collaborative thinking? This dissertation aims at explaining how neighbourhood sustainability frameworks are used in practice and to what extent they are contributing to the shift towards a more regenerative and resilient paradigm for the built environment. This qualitative research project is based on three in-depth case studies of ecodistrict pilot projects that have experimented with neighbourhood sustainability frameworks: first, the EcoDistricts pilot projects in Portland, Oregon; second, the BREEAM-Communities Masthusen pilot project in Malmö, Sweden; and third, the superblock pilot project and the Ecological Urbanism framework in the Sant Martí district of Barcelona, Spain. The research project uses a qualitative, exploratory approach in which semi-structured interviews with key stakeholders involved in the pilot projects helped to construct an analytical framework – the ‘process-based approach.’ The results show the number of ways in which the neighbourhood sustainability frameworks impact practice, specifically in terms of four dimensions: collaboration, community participation, stakeholder relations and visual communication. The case study results also shed light on the gaps between the expectations of the frameworks and the reality of how they are implemented. Indeed, external and internal barriers are identified in the case studies, which serve to explain why the frameworks are making only modest progress in the shift to a new paradigm. The results serve as a reminder that neighbourhood sustainability frameworks cannot be understood in a vacuum. On the contrary, how and when they are used, and how they are framed are equally important. The successful application of these frameworks depends on a variety of conditions that can lead to better collaboration, community participation, stakeholders relations and visual communication. These include for instance, the alignment of stakeholder assets, actions and expectations; fostering internal attributes in the planning teams; having conflict resolution mechanisms in place; and ensuring that community participation be not only deep but also early on enough to impact decisionmaking. Based on these results, the project proposes a “stakeholder support framework” for third generation neighbourhood sustainability frameworks. At a macrolevel, it proposes three guiding principles for neighbourhood sustainability frameworks. At a finer-grain scale, it provides a series of concepts and toolkits to help eco-district leaders develop more regenerative, resilient, and just processes

Enhancing Usability and Explainability of Data Systems

The recent growth of data science expanded its reach to an ever-growing user base of nonexperts, increasing the need for usability, understandability, and explainability in these systems. Enhancing usability makes data systems accessible to people with different skills and backgrounds alike, leading to democratization of data systems. Furthermore, proper understanding of data and data-driven systems is necessary for the users to trust the function of the systems that learn from data. Finally, data systems should be transparent: when a data system behaves unexpectedly or malfunctions, the users deserve proper explanation of what caused the observed incident. Unfortunately, most existing data systems offer limited usability and support for explanations: these systems are usable only by experts with sound technical skills, and even expert users are hindered by the lack of transparency into the systems\u27 inner workings and functions. The aim of my thesis is to bridge the usability gap between nonexpert users and complex data systems, aid all sort of users, including the expert ones, in data and system understanding, and provide explanations that help reason about unexpected outcomes involving data systems. Specifically, my thesis has the following three goals: (1) enhancing usability of data systems for nonexperts, (2) enable data understanding that can assist users in a variety of tasks such as achieving trust in data-driven machine learning, gaining data understanding, and data cleaning, and (3) explaining causes of unexpected outcomes involving data and data systems. For enhancing usability, we focus on example-driven user intent discovery. We develop systems based on example-driven interactions in two different settings: querying relational databases and personalized document summarization. Towards data understanding, we develop a new data-profiling primitive that can characterize tuples for which a machine-learned model is likely to produce untrustworthy predictions. We also develop an explanation framework to explain causes of such untrustworthy predictions. Additionally, this new data-profiling primitive enables interactive data cleaning. Finally, we develop two explanation frameworks, tailored to provide explanations in debugging data system components, including the data itself. The explanation frameworks focus on explaining the root cause of a concurrent application\u27s intermittent failure and exposing issues in the data that cause a data-driven system to malfunction