John Hooker

A guide to adapting and thriving within unfamiliar cultural settings challenges the notion that professional life interacts with culture only at the etiquette level, distinguishing between rule-based and relationship-based cultures while considering the roles of such factors as competition, security, and lifestyle. (Social Science)

Just over two thousand years ago, Julius Caesar set into motion events that would culminate in the conquest of the tribes of Gaul. It is to the coins of one of these tribes that this book addresses itself. The Coriosolites inhabited what is now Cotes-d'Armor in Brittany. The tribe has left a large number of coins: more than 20,000 are recorded, and no Celtic tribe is so well represented. Large hoards of these coins have been found in Jersey, Brittany, and Normandy. Foremost among these is the La Marquanderie hoard from Jersey, consisting of over 11,000 coins. The La Marquanderie hoard forms the basis of this book. The further strength of this engagingly-written study is its appeal to a wide range of interests: it is not just a catalogue of coins, but a case study of Celtic religious philosophy and aesthetics, referring to such apparently disparate subjects as poetry, physics, and psychology. (The Appendices show all the flow charts in addition to an Index of Design Elements and a concordance between the author's coin numbers and Rybot's. There is also a Quick Identification Chart with which any Coriosolite coin may be classified in a few minutes.)

A pioneering look at the fundamental role of logic in optimization and constraint satisfaction While recent efforts to combine optimization and constraint satisfaction have received considerable attention, little has been said about using logic in optimization as the key to unifying the two fields. Logic-Based Methods for Optimization develops for the first time a comprehensive conceptual framework for integrating optimization and constraint satisfaction, then goes a step further and shows how extending logical inference to optimization allows for more powerful as well as flexible modeling and solution techniques. Designed to be easily accessible to industry professionals and academics in both operations research and artificial intelligence, the book provides a wealth of examples as well as elegant techniques and modeling frameworks ready for implementation. Timely, original, and thought-provoking, Logic-Based Methods for Optimization: *Demonstrates the advantages of combining the techniques in problem solving *Offers tutorials in constraint satisfaction/constraint programming and logical inference *Clearly explains such concepts as relaxation, cutting planes, nonserial dynamic programming, and Bender’s decomposition *Reviews the necessary technologies for software developers seeking to combine the two techniques *Features extensive references to important computational studies *And much more

Merging logic and mathematics in deductive inference-an innovative, cutting-edge approach. Optimization methods for logical inference? Absolutely, say Vijay Chandru and John Hooker, two major contributors to this rapidly expanding field. And even though "solving logical inference problems with optimization methods may seem a bit like eating sauerkraut with chopsticks. . . it is the mathematical structure of a problem that determines whether an optimization model can help solve it, not the context in which the problem occurs." Presenting powerful, proven optimization techniques for logic inference problems, Chandru and Hooker show how optimization models can be used not only to solve problems in artificial intelligence and mathematical programming, but also have tremendous application in complex systems in general. They survey most of the recent research from the past decade in logic/optimization interfaces, incorporate some of their own results, and emphasize the types of logic most receptive to optimization methods-propositional logic, first order predicate logic, probabilistic and related logics, logics that combine evidence such as Dempster-Shafer theory, rule systems with confidence factors, and constraint logic programming systems. Requiring no background in logic and clearly explaining all topics from the ground up, Optimization Methods for Logical Inference is an invaluable guide for scientists and students in diverse fields, including operations research, computer science, artificial intelligence, decision support systems, and engineering.