Expert Systems

Causal inference is central to many areas of artificial intelligence, including complex reasoning, planning, knowledge-base construction, robotics, explanation, and fairness. An active community of researchers develops and enhances algorithms that learn causal models from data, and this work has produced a series of impressive technical advances. However, evaluation techniques for causal modeling algorithms have remained somewhat primitive, limiting what we can learn from experimental studies of algorithm performance, constraining the types of algorithms and model representations that researchers consider, and creating a gap between theory and practice. We argue for more frequent use of evaluation techniques that examine interventional measures rather than structural or observational measures, and that evaluate those measures on empirical data rather than synthetic data. We survey the current practice in evaluation and show that the techniques we recommend are rarely used in practice.

Statistical Relational Learning (SRL) methods are the most widely used techniques to generate distributional representations of the symbolic Knowledge Bases (KBs). These methods embed any given KB into a vector space by exploiting statistical similarities among its entities and predicates but without any guarantee of preserving the underlying logical structure of the KB. This, in turn, results in poor performance of logical reasoning tasks that are solved using such distributional representations. We present a novel approach called Embed2Reason (E2R) that embeds a symbolic KB into a vector space in a logical structure preserving manner. This approach is inspired by the theory of Quantum Logic.

In this work, we aim to leverage prior symbolic knowledge to improve the performance of deep models. We propose a graph embedding network that projects propositional formulae (and assignments) onto a manifold via an augmented Graph Convolutional Network (GCN). To generate semantically-faithful embeddings, we develop techniques to recognize node heterogeneity, and semantic regularization that incorporate structural constraints into the embedding. Experiments show that our approach improves the performance of models trained to perform entailment checking and visual relation prediction. Interestingly, we observe a connection between the tractability of the propositional theory representation and the ease of embedding.

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Smartphone owners are being given new rights to have their device repaired under laws introduced by the EU that could put an end to'throwaway culture'. Manufacturers will made to fix broken electronic devices under the EU's new Circular Economy Action Plan (CEAP), which will also cover the UK despite Brexit. The plan, unveiled on Wednesday by the European Commission, will give Europeans'the right to repair' by making devices easier to fix. The laws, which will also apply to tablets, laptops and printers, focus on a more circular economy – where electronic resources are kept in use as long as possible. Major tech companies making devices hard to fix, including Apple, Samsung and Huawei, is creating an electronic and electrical rubbish mountain – wasting resources and blighting the environment, say green campaigners.

Knowledge Graphs (KGs) are graph-structured knowledge bases storing factual information about real-world entities. Understanding the uniqueness of each entity is crucial to the analyzing, sharing, and reusing of KGs. Traditional profiling technologies encompass a vast array of methods to find distinctive features in various applications, which can help to differentiate entities in the process of human understanding of KGs. In this work, we present a novel profiling approach to identify distinctive entity features. The distinctiveness of features is carefully measured by a HAS model, which is a scalable representation learning model to produce a multi-pattern entity embedding. We fully evaluate the quality of entity profiles generated from real KGs. The results show that our approach facilitates human understanding of entities in KGs.

AI and deep learning have been shining in dealing with unstructured data, from natural language understanding and automatic knowledge base construction to classifying and generating images and videos. Structured data, however, which is trapped in business applications such as product repositories, transaction logs, ERP and CRM systems are being left behind! Tabular data is still being processed by an older generation of data science techniques, like rule-based systems or decision trees. These methods use handcrafted features, are tedious to maintain, and require lots of manually labelled data. While the recent advancement of AI advances allowed mining huge value out of unstructured data, it would be remiss to not pay the same attention to the value of structured data in driving business, revenues, health, security and even governance.

We all know the old adage, "When all you have is a hammer, everything looks like a nail." But not everything is a nail, especially when it comes to documents and content. When we work on a document, we must understand its format and what it is about. If I start working at the DMV, for example, I have to quickly understand their forms and what problems they address, the questions drivers have and the most appropriate answers to those questions. If I work for the Department of Agriculture, then I need a completely different set of tools.

Knowledge-based support vector machines (KBSVMs) incorporate advice from domain experts, which can improve generalization significantly. A major limitation that has not been fully addressed occurs when the expert advice is imperfect, which can lead to poorer models. We propose a model that extends KBSVMs and is able to not only learn from data and advice, but also simultaneously improve the advice. The proposed approach is particularly effective for knowledge discovery in domains with few labeled examples. The proposed model contains bilinear constraints, and is solved using two iterative approaches: successive linear programming and a constrained concave-convex approach.

We present the Multi-value Rule Set (MRS) for interpretable classification with feature efficient presentations. Compared to rule sets built from single-value rules, MRS adopts a more generalized form of association rules that allows multiple values in a condition. Rules of this form are more concise than classical single-value rules in capturing and describing patterns in data. Our formulation also pursues a higher efficiency of feature utilization, which reduces possible cost in data collection and storage. We propose a Bayesian framework for formulating an MRS model and develop an efficient inference method for learning a maximum a posteriori, incorporating theoretically grounded bounds to iteratively reduce the search space and improve the search efficiency.