This book proposes a consistent methodology for building intelligent systems. It puts forward several formal models for designing and implementing rules-based systems, and presents illustrative case studies of their applications. These include software engineering, business process systems, Semantic Web, and context-aware systems on mobile devices. Rules offer an intuitive yet powerful method for representing human knowledge, and intelligent systems based on rules have many important applications. However, their practical development requires proper techniques and models - a gap that this book effectively addresses.
This exciting new text reveals both the evolution of this programming paradigm since its inception and the impressively broad scope of current research in the field. The contributors to this book are all leading world experts in Logic Programming, and they deal with both theoretical and practical issues. They address such diverse topics as: computational molecular biology, machine learning, mobile computing, multi-agent systems, planning, numerical computing and dynamical systems, database systems, an alternative to the "formulas as types" approach, program semantics and analysis, and natural language processing. XXXXXXX Neuer Text Logic Programming was founded 25 years ago. This exciting book reveals both the evolution of this programming paradigm and its impressively broad scope of current research. The contributions by leading computer scientists deal with both theoretical and practical issues. They address diverse topics such as: computational molecular biology, machine learning, mobile computing, multi-agent systems, numerical computing and dynamical systems, database systems, program semantics, natural language processing, and promising future directions.
This book constitutes revised selected papers from the 22nd International Conference on Applications of Declarative Programming and Knowledge Management, INAP 2019, the 33rd Workshop on Logic Programming, WLP 2019, and the 27th Workshop on Functional and (Constraint) Logic Programming, WFLP 2019. The 15 full papers and 1 short paper presented in this volume were carefully reviewed and selected from 24 submissions. The contributions present current research activities in the areas of declarative languages and compilation techniques, in particular for constraint-based, logical and functional languages and their extensions, as well as discuss new approaches and key findings in constraint-solving, knowledge representation, and reasoning techniques.
This book gives an account oC the mathematical Coundations oC logic programming. I have attempted to make the book selC-contained by including prooCs of almost all the results needed. The only prerequisites are some Camiliarity with a logic programming language, such as PROLOG, and a certain mathematical maturity. For example, the reader should be Camiliar with induction arguments and be comCortable manipulating logical expressions. Also the last chapter assumes some acquaintance with the elementary aspects of metric spaces, especially properties oC continuous mappings and compact spaces. Chapter 1 presents the declarative aspects of logic programming. This chapter contains the basic material Crom first order logic and fixpoint theory which will be required. The main concepts discussed here are those oC a logic program, model, correct answer substitution and fixpoint. Also the unification algorithm is discussed in some detail. Chapter 2 is concerned with the procedural semantics oC logic programs. The declarative concepts are implemented by means oC a specialized Corm oC resolution, called SLD-resolution. The main results of this chapter concern the soundness and completeness oC SLD-resolution and the independence oC the computation rule. We also discuss the implications of omitting the occur check from PROLOG implementations. Chapter 3 discusses negation. Current PROLOG systems implement a form of negation by means of the negation as failure rule. The main results of this chapter are the soundness and completeness oC the negation as failure rule.
Intelligent agents are employed as the central characters in this introductory text. Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI. Neural networks, genetic programming, computer vision, heuristic search, knowledge representation and reasoning, Bayes networks, planning, and language understanding are each revealed through the growing capabilities of these agents. A distinguishing feature of this text is in its evolutionary approach to the study of AI. This book provides a refreshing and motivating synthesis of the field by one of AI's master expositors and leading researches. - An evolutionary approach provides a unifying theme - Thorough coverage of important AI ideas, old and new - Frequent use of examples and illustrative diagrams - Extensive coverage of machine learning methods throughout the text - Citations to over 500 references - Comprehensive index
This work introduces Prolog, beginning with the logical principles underlying the language, going on to explain how to apply these principles in the data structures of programming, how to utilize deduction and how to exercise control by means of lists, sets, recursion and operators.
When carefully selected and used, Domain-Specific Languages (DSLs) may simplify complex code, promote effective communication with customers, improve productivity, and unclog development bottlenecks. In Domain-Specific Languages, noted software development expert Martin Fowler first provides the information software professionals need to decide if and when to utilize DSLs. Then, where DSLs prove suitable, Fowler presents effective techniques for building them, and guides software engineers in choosing the right approaches for their applications. This book’s techniques may be utilized with most modern object-oriented languages; the author provides numerous examples in Java and C#, as well as selected examples in Ruby. Wherever possible, chapters are organized to be self-standing, and most reference topics are presented in a familiar patterns format. Armed with this wide-ranging book, developers will have the knowledge they need to make important decisions about DSLs—and, where appropriate, gain the significant technical and business benefits they offer. The topics covered include: How DSLs compare to frameworks and libraries, and when those alternatives are sufficient Using parsers and parser generators, and parsing external DSLs Understanding, comparing, and choosing DSL language constructs Determining whether to use code generation, and comparing code generation strategies Previewing new language workbench tools for creating DSLs
Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.
The manual describes LISP, a formal mathematical language. LISP differs from most programming languages in three important ways. The first way is in the nature of the data. The LISP language is designed primarily for symbolic data processing used for symbolic calculations in differential and integral calculus, electrical circuit theory, mathematical logic, game playing, and other fields of artificial intelligence. The manual describes LISP, a formal mathematical language. LISP differs from most programming languages in three important ways. The first way is in the nature of the data. In the LISP language, all data are in the form of symbolic expressions usually referred to as S-expressions, of indefinite length, and which have a branching tree-type of structure, so that significant subexpressions can be readily isolated. In the LISP system, the bulk of the available memory is used for storing S-expressions in the form of list structures. The second distinction is that the LISP language is the source language itself which specifies in what way the S-expressions are to be processed. Third, LISP can interpret and execute programs written in the form of S-expressions. Thus, like machine language, and unlike most other high level languages, it can be used to generate programs for further executions.
