This book is a survey of key issues in the theory of evaluation aimed at exhibiting and clarifying the rational nature of the thought-procedures involved. By means of theoretical analysis and explanatory case studies, this volume shows how evaluation is—or should be—a rational procedure directed at appropriate objectives. Above all, it maintains the objectivity of rational evaluation.

Reasoning in Boolean Networks provides a detailed treatment of recent research advances in algorithmic techniques for logic synthesis, test generation and formal verification of digital circuits. The book presents the central idea of approaching design automation problems for logic-level circuits by specific Boolean reasoning techniques. While Boolean reasoning techniques have been a central element of two-level circuit theory for many decades Reasoning in Boolean Networks describes a basic reasoning methodology for multi-level circuits. This leads to a unified view on two-level and multi-level logic synthesis. The presented reasoning techniques are applied to various CAD-problems to demonstrate their usefulness for today's industrially relevant problems. Reasoning in Boolean Networks provides lucid descriptions of basic algorithmic concepts in automatic test pattern generation, logic synthesis and verification and elaborates their intimate relationship to provide further intuition and insight into the subject. Numerous examples are provide for ease in understanding the material. Reasoning in Boolean Networks is intended for researchers in logic synthesis, VLSI testing and formal verification as well as for integrated circuit designers who want to enhance their understanding of basic CAD methodologies.

Practical argumentation is intelligent reasoning from an agent's goals and known circumstances , and from an action selected as a means, to arrive at a decision on what action to take. This book will appeal to a wide audience, from designers of multi-agent and robotics systems to social scientists.

Engaging and accessible, this book teaches readers how to use inferential statistical thinking to check their assumptions, assess evidence about their beliefs, and avoid overinterpreting results that may look more promising than they really are. It provides step-by-step guidance for using both classical (frequentist) and Bayesian approaches to inference. Statistical techniques covered side by side from both frequentist and Bayesian approaches include hypothesis testing, replication, analysis of variance, calculation of effect sizes, regression, time series analysis, and more. Students also get a complete introduction to the open-source R programming language and its key packages. Throughout the text, simple commands in R demonstrate essential data analysis skills using real-data examples. The companion website provides annotated R code for the book's examples, in-class exercises, teaching notes, and slide decks. Pedagogical Features *Playful, conversational style and gradual approach; suitable for students without strong math backgrounds. *End-of-chapter exercises based on real data supplied in the free R package. *Technical explanation and equation/output boxes. *Appendices on how to install R and work with the sample datasets.

First Published in 1995. Routledge is an imprint of Taylor & Francis, an informa company.