In recent years, non-monotonic Inductive Logic Programming has received growing interest. Specifically, several new learning frameworks and algorithms have been introduced for learning under the answer set semantics, allowing the learning of common-sense knowledge involving defaults and exceptions, which are essential aspects of human reasoning. In this paper, we present a noise-tolerant generalisation of the learning from answer sets framework. We evaluate our ILASP3 system, both on synthetic and on real datasets, represented in the new framework. In particular, we show that on many of the datasets ILASP3 achieves a higher accuracy than other ILP systems that have previously been applied to the datasets, including a recently proposed differentiable learning framework.

Learning of user preferences, as represented by, for example, Conditional Preference Networks (CP-nets), has become a core issue in AI research. Recent studies investigate learning of CP-nets from randomly chosen examples or from membership and equivalence queries. To assess the optimality of learning algorithms as well as to better understand the combinatorial structure of classes of CP-nets, it is helpful to calculate certain learning-theoretic information complexity parameters. This article focuses on the frequently studied case of learning from so-called swap examples, which express preferences among objects that differ in only one attribute. It presents bounds on or exact values of some well-studied information complexity parameters, namely the VC dimension, the teaching dimension, and the recursive teaching dimension, for classes of acyclic CP-nets. We further provide algorithms that learn tree-structured and general acyclic CP-nets from membership queries. Using our results on complexity parameters, we assess the optimality of our algorithms as well as that of another query learning algorithm for acyclic CP-nets presented in the literature. Our algorithms are near-optimal, and can, under certain assumptions, be adapted to the case when the membership oracle is faulty.

We provide a detailed example for modular ontology modeling based on ontology design patterns. It is similar to the Chess Ontology tutorial in [6], which we suggest to read first. We will be less verbose in this tutorial; we provide it because additional examples should be helpful for those interested in adopting the modular ontology modeling methodology - see [6] and the book [2] in which it is contained. We assume that the reader is familiar with the Web Ontology Language OWL [5, 4]. Before we dive into the actual modeling, let us present the general workflow which we recommend for ontology modeling, and which is the same as in [6]. The steps of this workflow are laid out in Figure 1. We will refer to these steps, and explain them in more detail, as we advance through the tutorial. Every ontology is designed for a purpose; this purpose may be defined by a use case, or by a set of use cases, or possibly by a set of potential use cases, which may include the future extensions or refinements of the ontology, and future reuse of the ontology by others. How specific should a use case be? Conventional wisdom may suggest that it is always better to be more specific. However, in the context of ontology modeling the case is not as clear-cut. A very specific use case may give rise to an ontology which is very specialized, i.e. modeling choices (so-called ontological commitments) may be made which fit only the very specific and detailed use case. As a consequence, later modifications, e.g. by widening the scope of the application (and therefore of the underlying ontology) become very cumbersome as they may conflict with ontological commitments made earlier.

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Rankings of universities and specialized academic programs have a major influence on students deciding what university to attend, faculty deciding where to work, government bodies deciding where and how to invest education and research funding, and university leaders deciding how to grow their institutions.9 There is general agreement in scientometrics that the quality of a university or a program depends on many factors, and different ranking metrics might be appropriate for different types of users. However, major points of contention emerge when it comes to agreeing on ranking methodology.20 Given the increasing impact of rankings, there is a need to better understand the actors influencing rankings and come up with a justifiable, transparent formula that encourages high-quality education and research at universities.11 We aim to contribute toward achieving this objective by focusing on ranking of the U.S. doctoral programs in computer science. We broadly group quality measures into objective (such as average research funding per faculty member) and subjective (such as peer assessment). The influential U.S. News ranking of computer science doctoral programsa is based purely on peer assessment in which computer science department chairs are asked to score other computer science programs on a scale of 1 to 5, with 1 being "marginal" and 5 being "outstanding," or enter "do not know" if not sufficiently familiar with the program. The final ranking is obtained by averaging the individual scores.

When we think of artificial intelligence, there is a hardwired imagery of gigantic thinking machines working in sci-fi environment. This imagery often comes from the science fiction that we have been watching or reading since childhood. However, deep diving suggests that artificial intelligence is an advanced form of algorithm that empowers machines to emulate human behaviour under real life situations. Today, there is no industry untouched by the ripples caused by artificial intelligence. Education sets the foundation of human behaviour.

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Over the past decades, researchers and ML practitioners have come up with better and better ways to build, understand and improve the quality of ML models, but mostly under the key assumption that the training data is distributed identically to the testing data. In many real-world applications, however, some potential training examples are unknown to the modeler, due to sample selection bias or, more generally, covariate shift, i.e., a distribution shift between the training and deployment stage. The resulting discrepancy between training and testing distributions leads to poor generalization performance of the ML model and hence biased predictions. We provide novel algorithms that estimate the number and properties of these unknown training examples---unknown unknowns. This information can then be used to correct the training set, prior to seeing any test data. The key idea is to combine species-estimation techniques with data-driven methods for estimating the feature values for the unknown unknowns. Experiments on a variety of ML models and datasets indicate that taking the unknown examples into account can yield a more robust ML model that generalizes better.

Kearns et al. [2018] recently proposed a notion of rich subgroup fairness intended to bridge the gap between statistical and individual notions of fairness. Rich subgroup fairness picks a statistical fairness constraint (say, equalizing false positive rates across protected groups), but then asks that this constraint hold over an exponentially or infinitely large collection of subgroups defined by a class of functions with bounded VC dimension. They give an algorithm guaranteed to learn subject to this constraint, under the condition that it has access to oracles for perfectly learning absent a fairness constraint. In this paper, we undertake an extensive empirical evaluation of the algorithm of Kearns et al. On four real datasets for which fairness is a concern, we investigate the basic convergence of the algorithm when instantiated with fast heuristics in place of learning oracles, measure the tradeoffs between fairness and accuracy, and compare this approach with the recent algorithm of Agarwal et al. [2018], which implements weaker and more traditional marginal fairness constraints defined by individual protected attributes. We find that in general, the Kearns et al. algorithm converges quickly, large gains in fairness can be obtained with mild costs to accuracy, and that optimizing accuracy subject only to marginal fairness leads to classifiers with substantial subgroup unfairness. We also provide a number of analyses and visualizations of the dynamics and behavior of the Kearns et al. algorithm. Overall we find this algorithm to be effective on real data, and rich subgroup fairness to be a viable notion in practice.

Scientists at the University of North Carolina at Chapel Hill have created a new method of particle tracking based on machine learning that is far more accurate and provides better automation than techniques currently in use. Single-particle tracking involves tracking the motion of individual particles, such as viruses, cells and drug-loaded nanoparticles, within fluids and biological samples. The technique is widely used in both physical and life sciences. The team at UNC-Chapel Hill that developed the new tracking method uses particle tracking to develop new ways to treat and prevent infectious diseases. They examine molecular interactions between antibodies and biopolymers and characterize and design nano-sized drug carriers.