Logic has acquired a reputation for difficulty, perhaps because many of the approaches adopted have been more suitable for mathematicians than computer scientists. This book shows that the subject is not inherently difficult and that the connections between logic and declarative language are straightforward. Many exercises have been included in the hope that these will lead to a much greater confidence in manual proofs, therefore leading to a greater confidence in automated proofs.

The idea of this book grew out of a symposium that was held at Stony Brook in September 2012 in celebration of David S.Warren's fundamental contributions to Computer Science and the area of Logic Programming in particular. Logic Programming (LP) is at the nexus of Knowledge Representation, Artificial Intelligence, Mathematical Logic, Databases, and Programming Languages. It is fascinating and intellectually stimulating due to the fundamental interplay among theory, systems, and applications brought about by logic. Logic programs are more declarative in the sense that they strive to be logical specifications of "what" to do rather than "how" to do it, and thus they are high-level and easier to understand and maintain. Yet, without being given an actual algorithm, LP systems implement the logical specifications automatically. Several books cover the basics of LP but focus mostly on the Prolog language with its incomplete control strategy and non-logical features. At the same time, there is generally a lack of accessible yet comprehensive collections of articles covering the key aspects in declarative LP. These aspects include, among others, well-founded vs. stable model semantics for negation, constraints, object-oriented LP, updates, probabilistic LP, and evaluation methods, including top-down vs. bottom-up, and tabling. For systems, the situation is even less satisfactory, lacking accessible literature that can help train the new crop of developers, practitioners, and researchers. There are a few guides onWarren's Abstract Machine (WAM), which underlies most implementations of Prolog, but very little exists on what is needed for constructing a state-of-the-art declarative LP inference engine. Contrast this with the literature on, say, Compilers, where one can first study a book on the general principles and algorithms and then dive in the particulars of a specific compiler. Such resources greatly facilitate the ability to start making meaningful contributions quickly. There is also a dearth of articles about systems that support truly declarative languages, especially those that tie into first-order logic, mathematical programming, and constraint solving. LP helps solve challenging problems in a wide range of application areas, but in-depth analysis of their connection with LP language abstractions and LP implementation methods is lacking. Also, rare are surveys of challenging application areas of LP, such as Bioinformatics, Natural Language Processing, Verification, and Planning. The goal of this book is to help fill in the previously mentioned void in the LP literature. It offers a number of overviews on key aspects of LP that are suitable for researchers and practitioners as well as graduate students. The following chapters in theory, systems, and applications of LP are included.

Baral shows how to write programs that behave intelligently, by giving them the ability to express knowledge and to reason. This book will appeal to practising and would-be knowledge engineers wishing to learn more about the subject in courses or through self-teaching.

Improve Your Creativity, Effectiveness, and Ultimately, Your Code In Modern Software Engineering, continuous delivery pioneer David Farley helps software professionals think about their work more effectively, manage it more successfully, and genuinely improve the quality of their applications, their lives, and the lives of their colleagues. Writing for programmers, managers, and technical leads at all levels of experience, Farley illuminates durable principles at the heart of effective software development. He distills the discipline into two core exercises: learning and exploration and managing complexity. For each, he defines principles that can help you improve everything from your mindset to the quality of your code, and describes approaches proven to promote success. Farley's ideas and techniques cohere into a unified, scientific, and foundational approach to solving practical software development problems within realistic economic constraints. This general, durable, and pervasive approach to software engineering can help you solve problems you haven't encountered yet, using today's technologies and tomorrow's. It offers you deeper insight into what you do every day, helping you create better software, faster, with more pleasure and personal fulfillment. Clarify what you're trying to accomplish Choose your tools based on sensible criteria Organize work and systems to facilitate continuing incremental progress Evaluate your progress toward thriving systems, not just more "legacy code" Gain more value from experimentation and empiricism Stay in control as systems grow more complex Achieve rigor without too much rigidity Learn from history and experience Distinguish "good" new software development ideas from "bad" ones Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Get to grips with Apple’s new SwiftUI framework for creating robust UIs for iOS and iPadOS using Swift programming Key FeaturesUse SwiftUI for building dynamic apps for Apple devices from scratchUnderstand declarative syntax in cross-platform development and how states work within SwiftUILearn to develop watchOS apps by reusing SwiftUI codeBook Description SwiftUI is the new and powerful interface toolkit that lets you design and build iOS, iPadOS, and macOS apps using declarative syntax. It is a powerful way to develop the UI elements of applications, which would normally be tightly coupled to application logic. Learn SwiftUI will get you up to speed with the framework and cross-device UI development in no time. Complete with detailed explanations and practical examples, this easy-to-follow guide will teach you the fundamentals of the SwiftUI toolkit. You'll learn how to build a powerful iOS and iPadOS application that can be reused for deployment on watchOS. As you progress, you'll delve into UI and unit testing in iOS apps, along with learning how to test your SwiftUI code for multiple devices. The book will also show you how to integrate SwiftUI features such as data binding and network requests into your current application logic. By the end of this book, you will have learned how to build a cross-device application using the SwiftUI framework and Swift programming. What you will learnExplore the fundamentals of SwiftUI and compare it with existing UI frameworksWrite SwiftUI syntax and understand what should and shouldn't be included in SwiftUI's layerAdd text and images to a SwiftUI view and decorate them using SwiftUI's modifiersCreate basic forms, and use camera and photo library functions to add images to themUnderstand the core concepts of Maps in iOS apps and add a MapView in SwiftUIDesign extensions within your existing apps to run them on watchOSHandle networking calls in SwiftUI to retrieve data from external sourcesWho this book is for This SwiftUI book helps any mobile app developer looking to understand the fundamentals of the new SwiftUI framework along with the benefits of cross-device development. A solid understanding of iOS and macOS app development, along with some knowledge of the Swift programming language, will be beneficial. Basic programming knowledge is essential to grasp the concepts covered in the book effectively.

This book constitutes the refereed proceedings of the Second International Workshop on Practical Aspects of Declarative Languages, PADL 2000, held in Boston, MA, USA in January 2000. The 21 revised full papers presented were carefully reviewed and selected from a total of 36 submissions. The papers are organized in topical sections on functional programming, functional-logic programming, logic programming, innovative applications, constraint programming and constraint solving, and systems applications.