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This book approaches to the subject of common-sense reasoning in AI using epistemic situation calculus which integrates the ideas of situation calculus and epistemic logic. Artificial intelligence (AI) is the research area of science and engineering for intelligent machines, especially intelligent computer programs. It is very important to deal with common-sense reasoning in knowledge-based systems. If we employ a logic-based framework, classical logic is not suited for the purpose of describing common-sense reasoning. It is well known that there are several difficulties with logic-based approaches, e.g., the so-called Fame Problem. We try to formalize common-sense reasoning in the context of granular computing based on rough set theory. The book is intended for those, like experts and students, who wish to get involved in the field as a monograph or a textbook for the subject. We assume that the reader has mastered the material ordinarily covered in AI and mathematical logic
A rigorous and comprehensive textbook covering the major approaches to knowledge graphs, an active and interdisciplinary area within artificial intelligence. The field of knowledge graphs, which allows us to model, process, and derive insights from complex real-world data, has emerged as an active and interdisciplinary area of artificial intelligence over the last decade, drawing on such fields as natural language processing, data mining, and the semantic web. Current projects involve predicting cyberattacks, recommending products, and even gleaning insights from thousands of papers on COVID-19. This textbook offers rigorous and comprehensive coverage of the field. It focuses systematically on the major approaches, both those that have stood the test of time and the latest deep learning methods.
Philosophers have long been fascinated by the connection between cause and effect: are causes things we can experience or are they concepts provided by our minds? The study of causation goes back to Aristotle, but resurged with David Hume and Immanuel Kant, and is now one of the most important topics in metaphysics. Most of the recent work done in this area has attempted to place causation in a deterministic, scientific worldview. But what about the unpredictable and chancey world we actually live in: can one theory of causation cover all instances of cause and effect?
ntil now there has been no state-of-the-art collection of themost important writings in automatic text summarization. This bookpresents the key developments in the field in an integrated frameworkand suggests future research areas. With the rapid growth of the World Wide Web and electronic information services, information is becoming available on-line at an incredible rate. One result is the oft-decried information overload. No one has time to read everything, yet we often have to make critical decisions based on what we are able to assimilate. The technology of automatic text summarization is becoming indispensable for dealing with this problem. Text summarization is the process of distilling the most important information from a source to produce an abridged version for a particular user or task. Until now there has been no state-of-the-art collection of the most important writings in automatic text summarization. This book presents the key developments in the field in an integrated framework and suggests future research areas. The book is organized into six sections: Classical Approaches, Corpus-Based Approaches, Exploiting Discourse Structure, Knowledge-Rich Approaches, Evaluation Methods, and New Summarization Problem Areas. Contributors D. A. Adams, C. Aone, R. Barzilay, E. Bloedorn, B. Boguraev, R. Brandow, C. Buckley, F. Chen, M. J. Chrzanowski, H. P. Edmundson, M. Elhadad, T. Firmin, R. P. Futrelle, J. Gorlinsky, U. Hahn, E. Hovy, D. Jang, K. Sparck Jones, G. M. Kasper, C. Kennedy, K. Kukich, J. Kupiec, B. Larsen, W. G. Lehnert, C. Lin, H. P. Luhn, I. Mani, D. Marcu, M. Maybury, K. McKeown, A. Merlino, M. Mitra, K. Mitze, M. Moens, A. H. Morris, S. H. Myaeng, M. E. Okurowski, J. Pedersen, J. J. Pollock, D. R. Radev, G. J. Rath, L. F. Rau, U. Reimer, A. Resnick, J. Robin, G. Salton, T. R. Savage, A. Singhal, G. Stein, T. Strzalkowski, S. Teufel, J. Wang, B. Wise, A. Zamora
To achieve successful communication, it is crucial to say clearly what we mean, but, at the same time, we need to pay attention to the form of our utterances, to avoid misunderstandings and the risk of offending our interlocutors. To avoid these pitfalls, we use a special category of utterances called 'indirect speech acts' (ISAs) that enable an optimal balance between clarity and politeness. But how do interpreters identify the meaning of these ISAs? And how does the social context influence the use of ISAs? This book attempts to answer these questions. It deals with the main theoretical and empirical questions surrounding the meaning and usage of ISAs, drawing on the latest research and neuroimaging data. Adopting a truly interdisciplinary perspective, it will appeal to students and scholars from diverse backgrounds, and anyone interested in exploring this phenomenon, which is so pervasive in our daily lives.