A New York Times Editors' Choice Book Named a Most Anticipated Book of 2022 by Literary Hub and Goodreads A playful history of the humble index and its outsized effect on our reading lives. Most of us give little thought to the back of the book—it’s just where you go to look things up. But as Dennis Duncan reveals in this delightful and witty history, hiding in plain sight is an unlikely realm of ambition and obsession, sparring and politicking, pleasure and play. In the pages of the index, we might find Butchers, to be avoided, or Cows that sh-te Fire, or even catch Calvin in his chamber with a Nonne. Here, for the first time, is the secret world of the index: an unsung but extraordinary everyday tool, with an illustrious but little-known past. Charting its curious path from the monasteries and universities of thirteenth-century Europe to Silicon Valley in the twenty-first, Duncan uncovers how it has saved heretics from the stake, kept politicians from high office, and made us all into the readers we are today. We follow it through German print shops and Enlightenment coffee houses, novelists’ living rooms and university laboratories, encountering emperors and popes, philosophers and prime ministers, poets, librarians and—of course—indexers along the way. Revealing its vast role in our evolving literary and intellectual culture, Duncan shows that, for all our anxieties about the Age of Search, we are all index-rakers at heart—and we have been for eight hundred years.

Case-based reasoning means reasoning based on remembering previous experiences. A reasoner using old experiences (cases) might use those cases to suggest solutions to problems, to point out potential problems with a solution being computed, to interpret a new situation and make predictions about what might happen, or to create arguments justifying some conclusion. A case-based reasoner solves new problems by remembering old situations and adapting their solutions. It interprets new situations by remembering old similar situations and comparing and contrasting the new one to old ones to see where it fits best. Case-based reasoning combines reasoning with learning. It spans the whole reasoning cycle. A situation is experienced. Old situations are used to understand it. Old situations are used to solve a problem (if there is one to be solved). Then the new situation is inserted into memory alongside the cases it used for reasoning, to be used another time. The key to this reasoning method, then, is remembering. Remembering has two parts: integrating cases or experiences into memory when they happen and recalling them in appropriate situations later on. The case-based reasoning community calls this related set of issues the indexing problem. In broad terms, it means finding in memory the experience closest to a new situation. In narrower terms, it can be described as a two-part problem: assigning indexes or labels to experiences when they are put into memory that describe the situations to which they are applicable, so that they can be recalled later; and at recall time, elaborating the new situation in enough detail so that the indexes it would have if it were in the memory are identified. Case-Based Learning is an edited volume of original research comprising invited contributions by leading workers. This work has also been published as a special issues of MACHINE LEARNING, Volume 10, No. 3.

This book elaborates the artificial intelligence theories behind the Case-Based Teaching Architecture developed at the Institute for the Learning Sciences. The book is for anyone who wants a technical discussion, but who is not a trained AI researcher.

This volume features the complete text of all regular papers, posters, and summaries of symposia presented at the 17th annual meeting of the Cognitive Science Society.

“The newbie investor will not find a better guide to personal finance.” —Burton Malkiel, author of A RANDOM WALK DOWN WALL STREET TV analysts and money managers would have you believe your finances are enormously complicated, and if you don’t follow their guidance, you’ll end up in the poorhouse. They’re wrong. When University of Chicago professor Harold Pollack interviewed Helaine Olen, an award-winning financial journalist and the author of the bestselling Pound Foolish, he made an off­hand suggestion: everything you need to know about managing your money could fit on an index card. To prove his point, he grabbed a 4" x 6" card, scribbled down a list of rules, and posted a picture of the card online. The post went viral. Now, Pollack teams up with Olen to explain why the ten simple rules of the index card outperform more complicated financial strategies. Inside is an easy-to-follow action plan that works in good times and bad, giving you the tools, knowledge, and confidence to seize control of your financial life.

Mathematical theorem proving has undergone an impressive development during the last two decades, resulting in a variety of powerful systems for applications in mathematical deduction and knowledge processing. Natural language processing has become a topic of outstanding relevance in information technology, mainly due to the explosive growth of the Web, where by far the largest part of information is encoded in natural language documents. This monograph focuses on the development of inference tools tailored to applications in natural language processing by demonstrating how the model generation paradigm can be used as a framework for the support of specific tasks in natural language interpretation and natural language based inference in a natural way. The book appears at a pivotal moment, when much attention is being paid to the task of adding a semantic layer to the Web, and representation and processing of natural language based semantic information pops up as a primary requirement for further technological progress.

First Published in 1994. Routledge is an imprint of Taylor & Francis, an informa company.