While creativity plays an important role in the advancement of computer science, great ideas are built on a foundation of practical experience and knowledge. This book presents programming techniques which will be useful in both AI projects and more conventional software engineering endeavors. My primary goal is to enter tain, to introduce new technologies and to provide reusable software modules for the computer programmer who enjoys using programs as models for solutions to hard and interesting problems. If this book succeeds in entertaining, then it will certainly also educate. I selected the example application areas covered here for their difficulty and have provided both program examples for specific applications and (I hope) the method ology and spirit required to master problems for which there is no obvious solution. I developed the example programs on a Macintosh TM using the Macintosh Common LISP TM development system capturing screen images while the example programs were executing. To ensure portability to all Common LISP environments, I have provided a portable graphics library in Chapter 2. All programs in this book are copyrighted by Mark Watson. They can be freely used in any free or commercial software systems if the following notice appears in the fine print of the program's documentation: "This program contains software written by Mark Watson." No royalties are required. The program miniatures contained in this book may not be distributed by posting in source code form on public information networks, or in printed form without my written permission.

The defacto standard - a must-have for all LISP programmers. In this greatly expanded edition of the defacto standard, you'll learn about the nearly 200 changes already made since original publication - and find out about gray areas likely to be revised later. Written by the Vice- Chairman of X3J13 (the ANSI committee responsible for the standardization of Common Lisp) and co-developer of the language itself, the new edition contains the entire text of the first edition plus six completely new chapters. They cover: - CLOS, the Common Lisp Object System, with new features to support function overloading and object-oriented programming, plus complete technical specifications * Loops, a powerful control structure for multiple variables * Conditions, a generalization of the error signaling mechanism * Series and generators * Plus other subjects not part of the ANSI standards but of interest to professional programmers. Throughout, you'll find fresh examples, additional clarifications, warnings, and tips - all presented with the author's customary vigor and wit.

[The book] provides a balanced survey of the fundamentals of artificial intelligence, emphasizing the relationship between symbolic and numeric processing. The text is structured around an innovative, interactive combination of LISP programming and AI; it uses the constructs of the programming language to help readers understand the array of artificial intelligence concepts presented. After an overview of the field of artificial intelligence, the text presents the fundamentals of LISP, explaining the language's features in more detail than any other AI text. Common Lisp is then used consistently, in both programming exercises and plentiful examples of actual AI code.- Back cover This text is intended to provide an introduction to both AI and LISp for those having a background in computer science and mathematics. -Pref.

This book constitutes the refereed proceedings of the 11th International Symposium on Practical Aspects of Declarative Languages, PADL 2009, held in Savannah, GA, USA, in January 2009, colocated with POPL 2009, the Symposium on Principles of Programming Languages. The 18 revised full papers presented together with 1 invited talk were carefully reviewed and selected from 48 submissions. The volume features original work emphasizing novel applications and implementation techniques for all forms of declarative concepts, including functions, relations, logic, and constraints. The papers address all current aspects of declarative programming; they are organized in topical sections on user interfaces and environments, networks and data, multi-threading and parallelism, databases and large data sets, tabling and optimization, as well as language extensions and implementation.

This book is the third volume in a series that provides a hands-on perspective on the evolving theories associated with Roger Schank and his students. The primary focus of this volume is on constructing explanations. All of the chapters relate to the problem of building computer programs that can develop hypotheses about what might have caused an observed event. Because most researchers in natural language processing don't really want to work on inference, memory, and learning issues, most of their sample text fragments are chosen carefully to de-emphasize the need for non text-related reasoning. The ability to come up with hypotheses about what is really going on in a story is a hallmark of human intelligence. The biggest difference between truly intelligent readers and less intelligent ones is the extent to which the reader can go beyond merely understanding the explicit statements being communicated. Achieving a creative level of understanding means developing hypotheses about questions for which there may be no conclusively correct answer at all. The focus of the lab, during the period documented in this book, was to work on getting a computer program to do that. The volume adopts a case-based approach to the construction of explanations which suggests that the main steps in the process of explaining a given anomaly are as follows: * Retrieve an explanation that might be relevant to the anomaly. * Evaluate whether the retrieved explanation makes sense when applied to the current anomaly. * Adapt the explanation to produce a new variant that fits better if the retrieved explanation doesn't fit the anomaly perfectly.

An indispensable collection of practical tips and real-world advice for tackling common Python problems and taking your code to the next level. Features interviews with high-profile Python developers who share their tips, tricks, best practices, and real-world advice gleaned from years of experience. Sharpen your Python skills as you dive deep into the Python programming language with Serious Python. You'll cover a range of advanced topics like multithreading and memorization, get advice from experts on things like designing APIs and dealing with databases, and learn Python internals to help you gain a deeper understanding of the language itself. Written for developers and experienced programmers, Serious Python brings together over 15 years of Python experience to teach you how to avoid common mistakes, write code more efficiently, and build better programs in less time. As you make your way through the book's extensive tutorials, you'll learn how to start a project and tackle topics like versioning, layouts, coding style, and automated checks. You'll learn how to package your software for distribution, optimize performance, use the right data structures, define functions efficiently, pick the right libraries, build future-proof programs, and optimize your programs down to the bytecode. You'll also learn how to: - Make and use effective decorators and methods, including abstract, static, and class methods - Employ Python for functional programming using generators, pure functions, and functional functions - Extend flake8 to work with the abstract syntax tree (AST) to introduce more sophisticated automatic checks into your programs - Apply dynamic performance analysis to identify bottlenecks in your code - Work with relational databases and effectively manage and stream data with PostgreSQL If you've been looking for a way to take your Python skills from good to great, Serious Python will help you get there. Learn from the experts and get seriously good at Python with Serious Python!

1. BASIC CONCEPTS OF INTERACTIVE THEOREM PROVING Interactive Theorem Proving ultimately aims at the construction of powerful reasoning tools that let us (computer scientists) prove things we cannot prove without the tools, and the tools cannot prove without us. Interaction typi cally is needed, for example, to direct and control the reasoning, to speculate or generalize strategic lemmas, and sometimes simply because the conjec ture to be proved does not hold. In software verification, for example, correct versions of specifications and programs typically are obtained only after a number of failed proof attempts and subsequent error corrections. Different interactive theorem provers may actually look quite different: They may support different logics (first-or higher-order, logics of programs, type theory etc.), may be generic or special-purpose tools, or may be tar geted to different applications. Nevertheless, they share common concepts and paradigms (e.g. architectural design, tactics, tactical reasoning etc.). The aim of this chapter is to describe the common concepts, design principles, and basic requirements of interactive theorem provers, and to explore the band width of variations. Having a 'person in the loop', strongly influences the design of the proof tool: proofs must remain comprehensible, - proof rules must be high-level and human-oriented, - persistent proof presentation and visualization becomes very important.

The British philosopher Stephan Toulmin, in his The Uses of Argument, made the provocative claim that "logic is generalized jurisprudence". For Toulmin, logic is the study of nonns for practical argumentation and decision making. In his view, mathematical logicians were preoccupied with fonnalizing the concepts of logical necessity, consequence and contradiction, at the expense of other equally important issues, such as how to allocate the burden of proof and make rational decisions given limited resources. He also considered it a mistake to look primarily to psychology, linguistics or the cognitive sciences for answers to these fundamentally nonnative questions. Toulmin's concerns about logic, writing in the 1950's, are equally applicable to the field of Artificial Intelligence today. The mainstream of Artificial Intelligence has focused on the analytical and empirical aspects of intelligence, without giving adequate attention to the nonnative, regulative functions of knowledge representation, problem solving and decision-making. Nonnative issues should now be of even greater interest, with the shift in perspective of AI from individual to collective intelligence, in areas such as multi-agent systems, cooperative design, distributed artificial intelligence, and computer-supported cooperative work. Networked "virtual societies" of humans and software agents would also require "virtual legal systems" to fairly balance interests, resolve conflicts, and promote security.

This book is based on the author's Ph.D. thesis which was selected during the 1994 ACM Doctoral Dissertation Competition as one of the two co-winning works. T.V. Raman did his Ph.D. work at Cornell University with Professor Davied Gries as thesis advisor. The author presents the computing system ASTER that audio formats electronic documents to produce audio documents. ASTER can speak both literary texts and highly technical documents containing complex mathematics (presented in (LA)TEX).