The recent years have witnessed the rise of accurate but obscure decision systems which hide the logic of their internal decision processes to the users. The lack of explanations for the decisions of black box systems is a key ethical issue, and a limitation to the adoption of machine learning components in socially sensitive and safety-critical contexts. In this paper we focus on the problem of black box outcome explanation, i.e., explaining the reasons of the decision taken on a specific instance. We propose LORE, an agnostic method able to provide interpretable and faithful explanations. LORE first leans a local interpretable predictor on a synthetic neighborhood generated by a genetic algorithm. Then it derives from the logic of the local interpretable predictor a meaningful explanation consisting of: a decision rule, which explains the reasons of the decision; and a set of counterfactual rules, suggesting the changes in the instance's features that lead to a different outcome. Wide experiments show that LORE outperforms existing methods and baselines both in the quality of explanations and in the accuracy in mimicking the black box.

This paper considers the learning of Boolean rules in either disjunctive normal form (DNF, OR-of-ANDs, equivalent to decision rule sets) or conjunctive normal form (CNF, AND-of-ORs) as an interpretable model for classification. An integer program is formulated to optimally trade classification accuracy for rule simplicity. Column generation (CG) is used to efficiently search over an exponential number of candidate clauses (conjunctions or disjunctions) without the need for heuristic rule mining. This approach also bounds the gap between the selected rule set and the best possible rule set on the training data. To handle large datasets, we propose an approximate CG algorithm using randomization. Compared to three recently proposed alternatives, the CG algorithm dominates the accuracy-simplicity trade-off in 7 out of 15 datasets. When maximized for accuracy, CG is competitive with rule learners designed for this purpose, sometimes finding significantly simpler solutions that are no less accurate.

Mathematics has played a substantial role to revolutionize the medical science. Intelligent systems based on mathematical theories have proved to be efficient in diagnosing various diseases. In this paper, we used an expert system based on soft set theory and fuzzy set theory named as a soft expert system to diagnose tropical disease dengue. The objective to use soft expert system is to predict the risk level of a patient having dengue fever by using input variables like age, TLC, SGOT, platelets count and blood pressure. The proposed method explicitly demonstrates the exact percentage of the risk level of dengue fever automatically circumventing for all possible (medical) imprecisions.

The crux of the problem is that the field of artificial intelligence has not come to grips with the infinite complexity of language. Just as you can make infinitely many arithmetic equations by combining a few mathematical symbols and following a small set of rules, you can make infinitely many sentences by combining a modest set of words and a modest set of rules. A genuine, human-level A.I. will need to be able to cope with all of those possible sentences, not just a small fragment of them. The narrower the scope of a conversation, the easier it is to have. If your interlocutor is more or less following a script, it is not hard to build a computer program that, with the help of simple phrase-book-like templates, can recognize a few variations on a theme.

The interest in Artificial Intelligence (AI) is constantly growing, especially in industries characterized by repetitive processes and manual tasks. One benefit of the effective application of AI is that humans, freed from these repetitive tasks, are able to focus on higher-value activities. This is clear to the 41% of decision makers who are using cognitive and AI tools in their business, according to a recnet a Forrester survey, "TITLE," as shown in Figure 1. In fact, many decision makers consider the technological progression to AI a major priority and already understand the potential of AI for their business. Automation is an opportunity for financial industry, where AI can reduce the efforts of finance professionals in traditional activities such as transaction processing, auditing and compliance.

I am by no means a particularly good example of study habits, but generally I tend to read what I need and go from there... Basically this in practice often means starting somewhere relevant to whatever work/assignment/project I'm trying to do, and then going backwards building a recursive stack of readings that seem important to understanding the previous thing until I reach a point where I am familiar with the material already. Then I work through the stack until I'm back to wherever I started. Essentially this is the backward chaining algorithm. I also, if I need to learn a lot from a book for some reason (i.e. a course) or have no particular goal in mind but find my self with a text that piques my interest, then I tend to skim from cover to cover everything that actually attracts my attention, occasionally flipping back to something that I realize is important for understanding later stuff. If it seems especially critical and I can't understand it, then I'll look through exercises and maybe do them if it seems worthwhile.

Neural networks have been learning complex multi-hop reasoning in various domains. One such formal setting for reasoning, logic, provides a challenging case for neural networks. In this article, we propose a Neural Inference Network (NIN) for learning logical inference over classes of logic programs. Trained in an end-to-end fashion NIN learns representations of normal logic programs, by processing them at a character level, and the reasoning algorithm for checking whether a logic program entails a given query. We define 12 classes of logic programs that exemplify increased level of complexity of the inference process (multi-hop and default reasoning) and show that our NIN passes 10 out of the 12 tasks. We also analyse the learnt representations of logic programs that NIN uses to perform the logical inference.

Recent use includes the assassination with VX of Kim Jong Nam, half-brother of North Korean leader Kim Jong Un, in Kuala Lumpur airport in February 2017 and the attempted murder of Sergei Skripal, a 66-year-old former Russian double agent, and his daughter with a Novichok nerve agent in March in England.

This track includes data-related tasks such as analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy, with special focus on social data on the web. Hence, the broader context of the track comprehends AI, web mining, information retrieval, natural language processing, and sentiment analysis. As the web rapidly evolves, web users are evolving with it. In an era of social connectedness, people are becoming increasingly enthusiastic about interacting, sharing, and collaborating through social networks, online communities, blogs, wikis, and other online collaborative media. In recent years, this collective intelligence has spread to many different areas, with particular focus on fields related to everyday life such as commerce, tourism, education, and health, causing the size of the social web to expand exponentially. The distillation of knowledge from such a large amount of unstructured information, however, is an extremely difficult task, as the contents of today’s web are perfectly suitable for human consumption, but remain hardly accessible to machines. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from marketing and financial market prediction. The primary aim of this track is exploring the new frontiers of big data computing for opinion mining and sentiment analysis through machine learning techniques, knowledge-based systems, adaptive and transfer learning, in order to more efficiently retrieve and extract social information from the web.