Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness. Association Rules find all sets of items (itemsets) that have support greater than the minimum support and then using the large itemsets to generate the desired rules that have confidence greater than the minimum confidence. The lift of a rule is the ratio of the observed support to that expected if X and Y were independent. A typical and widely used example of association rules application is market basket analysis.

The paper describes an ongoing effort in developing a declarative system for supporting operators in the Nuclear Power Plant (NPP) control room. The focus is on two modules: diagnosis and explanation of events that happened in NPPs. We describe an Answer Set Programming (ASP) representation of an NPP, which consists of declarations of state variables, components, their connections, and rules encoding the plant behavior. We then show how the ASP program can be used to explain the series of events that occurred in the Three Mile Island, Unit 2 (TMI-2) NPP accident, the most severe accident in the USA nuclear power plant operating history. We also describe an explanation module aimed at addressing answers to questions such as ``why an event occurs?'' or ``what should be done?'' given the collected data. This paper is *under consideration* for acceptance in TPLP Journal.

Many of us have had the feeling that technology, which continues to change at an ever-dizzying pace, may be leaving us behind. That was embodied this past week during a Congressional hearing, nominally convened to investigate antitrust concerns of four big tech titans: Amazon, Apple, Facebook and Google. While the five-and-a-half-hour inquiry touched on a range topics from pesky spam filters and search results to how companies approached acquisitions, the House Judiciary subcommittee hearing laid one thing bare: A sizable disconnect appears to exist between the technology Americans are using and depending on in their daily lives and the knowledge base of people with the power and responsibility to decide its future and regulation. "Consumers and investors walk away feeling like a lot of these lawmakers don't really understand the business models to an extent that they could then navigate them and put laws in place that will dictate the future of where they go," said Daniel Ives, an analyst with Wedbush Securities. The antitrust subcommittee hearing had been convened to look into the tech giants' market dominance.

End-users' trust in automated agents is important as automated decision-making and planning is increasingly used in many aspects of people's lives. In real-world applications of planning, multiple optimization objectives are often involved. Thus, planning agents' decisions can involve complex tradeoffs among competing objectives. It can be difficult for the end-users to understand why an agent decides on a particular planning solution on the basis of its objective values. As a result, the users may not know whether the agent is making the right decisions, and may lack trust in it. In this work, we contribute an approach, based on contrastive explanation, that enables a multi-objective MDP planning agent to explain its decisions in a way that communicates its tradeoff rationale in terms of the domain-level concepts. We conduct a human subjects experiment to evaluate the effectiveness of our explanation approach in a mobile robot navigation domain. The results show that our approach significantly improves the users' understanding, and confidence in their understanding, of the tradeoff rationale of the planning agent.

As machine learning is increasingly used to help make decisions, there is a demand for these decisions to be explainable. Arguably, the most explainable machine learning models use decision rules. This paper focuses on decision sets, a type of model with unordered rules, which explains each prediction with a single rule. In order to be easy for humans to understand, these rules must be concise. Earlier work on generating optimal decision sets first minimizes the number of rules, and then minimizes the number of literals, but the resulting rules can often be very large. Here we consider a better measure, namely the total size of the decision set in terms of literals. So we are not driven to a small set of rules which require a large number of literals. We provide the first approach to determine minimum-size decision sets that achieve minimum empirical risk and then investigate sparse alternatives where we trade accuracy for size. By finding optimal solutions we show we can build decision set classifiers that are almost as accurate as the best heuristic methods, but far more concise, and hence more explainable.

Every Neuropod model implements a problem definition -- a formal description of a problem for models to solve. As a result, any models that solve the same problem are interchangeable even if they use different frameworks. Existing models can be wrapped in a Neuropod package, which contains the original model along with metadata, test data, and custom ops if any. Since its internal release in early 2019, hundred of Neuropod models have been deployed across Uber ATG, Uber AI, and the core Uber business -- including models for demand forecasting, estimated time of arrival (ETA) prediction for rides, menu transcription for Uber Eats, and object detection models for self-driving vehicles. Neuropod makes it easy for researchers to build models in a framework of their choosing while also simplifying product-ionization of these models, says the company.

Simply put, for the last 200 years, advisers have worked on the principle of information asymmetry, where they have better information than their clients. Today, we are at the point where machine intelligence is gaining information asymmetry over advisers, and that's only going to get more acute and asymmetrical as time goes on. The only possible hope for human advisers is that they co-opt machine intelligence into their process.

The global Artificial Intelligence company Expert System announced the release of the expert.ai NL API, the cloud-based Natural Language API that enables data scientists, computational linguists, knowledge engineers and developers to easily embed advanced Natural Language Understanding and Natural Language Processing capabilities (NLU / NLP) into their applications. This release is the first step in executing on the company's strategy to become the global platform of reference for AI-based Natural Language problem solving. The growing need for accessible and accurate AI-based NLU / NLP applications in the enterprise places increased demand on the developer ecosystem to bring speed, scale and precision to linguistic analysis. According to Gartner, "during recent years, advances in the application of machine learning (including neural networks) and knowledge graphs to natural language processing have enabled machine-based attribution that diminishes the need for human oversight. Application of the technology is broadening as well as deepening -- across industries and functional domains, and into use cases -- pushing this innovation from many years in the Tough of Disillusionment toward the Slope of Enlightenment."

Expert System's Cogito is the only Natural Language Understanding AI technology that provides a human-like understanding of the meaning of each word in a text. Cogito leverages the deepest text analysis, starting from linguistics (morphological, grammatical and syntactical analysis) to semantics, including word disambiguation and an embedded, domain-independent and pre-trained linguistic model (Knowledge Graph). This translates into the fastest, most accurate and cost effective implementation of AI in the enterprise.

Unsatisfying accuracy of learning methods is mostly caused by omitting the influence of important parameters such as membership assignments, type of data objects, and distance or similarity functions. The proposed method, called Bounded Fuzzy Possibilistic Method (BFPM) addresses different issues that previous clustering or classification methods have not sufficiently considered in their membership assignments. In fuzzy methods, the object's memberships should sum to 1. Hence, any data object may obtain full membership in at most one cluster or class. Possibilistic methods relax this condition, but the method can be satisfied with the results even if just an arbitrary object obtains the membership from just one cluster, which prevents the objects' movement analysis. Whereas, BFPM differs from previous fuzzy and possibilistic approaches by removing these restrictions. Furthermore, BFPM provides the flexible search space for objects' movement analysis. Data objects are also considered as fundamental keys in learning methods, and knowing the exact type of objects results in providing a suitable environment for learning algorithms. The Thesis introduces a new type of object, called critical, as well as categorizing data objects into two different categories: structural-based and behavioural-based. Critical objects are considered as causes of miss-classification and miss-assignment in learning procedures. The Thesis also proposes new methodologies to study the behaviour of critical objects with the aim of evaluating objects' movements (mutation) from one cluster or class to another. The Thesis also introduces a new type of feature, called dominant, that is considered as one of the causes of miss-classification and miss-assignments. Then the Thesis proposes new sets of similarity functions, called Weighted Feature Distance (WFD) and Prioritized Weighted Feature Distance (PWFD).