Home robots may come with many sophisticated built-in abilities, however there will always be a degree of customization needed for each user and environment. Ideally this should be accomplished through one-shot learning, as collecting the large number of examples needed for statistical inference is tedious. A particularly appealing approach is to simply explain to the robot, via speech, what it should be doing. In this paper we describe the ALIA cognitive architecture that is able to effectively incorporate user-supplied advice and prohibitions in this manner. The functioning of the implemented system on a small robot is illustrated by an associated video [11]. 1 INTRODUCTION A typical home robot of the future might have built-in navigation, object recognition, task planning, and dexterous manipulation. Y et, despite these sophisticated capabilities, there are still things it cannot know when it first arrives. For instance, what a particular room in the house is called, even if it can identify the general type.

OneClass SVM is a popular method for unsupervised anomaly detection. As many other methods, it suffers from the \textit{black box} problem: it is difficult to justify, in an intuitive and simple manner, why the decision frontier is identifying data points as anomalous or non anomalous. Such type of problem is being widely addressed for supervised models. However, it is still an uncharted area for unsupervised learning. In this paper, we describe a method to infer rules that justify why a point is labelled as an anomaly, so as to obtain intuitive explanations for models created using the OneClass SVM algorithm. We evaluate our proposal with different datasets, including real-world data coming from industry. With this, our proposal contributes to extend Explainable AI techniques to unsupervised machine learning models.

Representation learning on a knowledge graph (KG) is to embed entities and relations of a KG into low-dimensional continuous vector spaces. Early KG embedding methods only pay attention to structured information encoded in triples, which would cause limited performance due to the structure sparseness of KGs. Some recent attempts consider paths information to expand the structure of KGs but lack explainability in the process of obtaining the path representations. In this paper, we propose a novel Rule and Path-based Joint Embedding (RPJE) scheme, which takes full advantage of the explainability and accuracy of logic rules, the generalization of KG embedding as well as the supplementary semantic structure of paths. Specifically, logic rules of different lengths (the number of relations in rule body) in the form of Horn clauses are first mined from the KG and elaborately encoded for representation learning. Then, the rules of length 2 are applied to compose paths accurately while the rules of length 1 are explicitly employed to create semantic associations among relations and constrain relation embeddings. Besides, the confidence level of each rule is also considered in optimization to guarantee the availability of applying the rule to representation learning. Extensive experimental results illustrate that RPJE outperforms other state-of-the-art baselines on KG completion task, which also demonstrate the superiority of utilizing logic rules as well as paths for improving the accuracy and explainability of representation learning.

We investigate the application of inconsistency measures to the problem of analysing business rule bases. Due to some i ntri-cacies of the domain of business rule bases, a straightforwa rd application is not feasible. We therefore develop some new rat ionality postulates for this setting as well as adapt and modify exist ing inconsistency measures. We further adapt the notion of inconsistency values (or culpability measures) for this setting and give a comprehensive feasibility study.

Data: As with all ML applications, quality data is foundational to building anti-fraud ML systems. Data sets are only growing larger, and as the volumes increase, so does the challenge of detecting fraud. Thankfully the adage that more data equals better models is true when it comes to fraud detection. The make-or-break factor is having a ML platform that can scale as data and complexity increase. Multiplicity: There's no single ML algorithm or method that works best for fraud detection.

Inferring missing edges in multi-relational knowledge graphs is a fundamental task in statistical relational learning. However, previous work has largely focused on the transductive relation prediction problem, where missing edges must be predicted for a single, fixed graph. In contrast, many real-world situations require relation prediction on dynamic or previously unseen knowledge graphs (e.g., for question answering, dialogue, or e-commerce applications). Here, we develop a novel graph neural network (GNN) architecture to perform inductive relation prediction and provide a systematic comparison between this GNN approach and a strong, rule-based baseline. Our results highlight the significant difficulty of inductive relational learning, compared to the transductive case, and offer a new challenging set of inductive benchmarks for knowledge graph completion.

We present a novel method - LIBRE - to learn an interpretable classifier, which materializes as a set of Boolean rules. LIBRE uses an ensemble of bottom-up weak learners operating on a random subset of features, which allows for the learning of rules that generalize well on unseen data even in imbalanced settings. Weak learners are combined with a simple union so that the final ensemble is also interpretable. Experimental results indicate that LIBRE efficiently strikes the right balance between prediction accuracy, which is competitive with black box methods, and interpretability, which is often superior to alternative methods from the literature.

As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a human interpretable manner. It has recently become apparent that a high-fidelity explanation of a black box ML model may not accurately reflect the biases in the black box. As a consequence, explanations have the potential to mislead human users into trusting a problematic black box. In this work, we rigorously explore the notion of misleading explanations and how they influence user trust in black box models. More specifically, we propose a novel theoretical framework for understanding and generating misleading explanations, and carry out a user study with domain experts to demonstrate how these explanations can be used to mislead users. Our work is the first to empirically establish how user trust in black box models can be manipulated via misleading explanations.

The worst foreign-policy decision by the United States of the last generation – and perhaps longer – was the "war of choice" that it launched in Iraq in 2003 for the stated purpose of eliminating weapons of mass destruction that did not, in fact, exist. Understanding the illogic behind that disastrous decision has never been more relevant, because it is being used to justify a similarly misguided US policy today. The decision to invade Iraq followed the illogic of then-US vice-president Richard Cheney, who declared that even if the risk of WMD falling into terrorist hands was tiny – say, 1% – we should act as if that scenario would certainly occur. Such reasoning is guaranteed to lead to wrong decisions more often than not. Yet the US and some of its allies are now using the Cheney Doctrine to attack Chinese technology.

People often talk about artificial intelligence as if it were all one thing. It's more accurate to think of AI as a collection of approaches to problems. An AI system usually combines several approaches, doing whatever produces the best results. The design of a piece of software needs to decide what problem domain it will cover. In other words, what class of problems will it deal with? The narrower the domain is, the easier the job.