One of the most important functions of artificial intelligence, automated problem solving, consists mainly of the development of software systems designed to find solutions to problems. These systems utilize a search space and algorithms in order to reach a solution. Artificial Intelligence for Advanced Problem Solving Techniques offers scholars and practitioners cutting-edge research on algorithms and techniques such as search, domain independent heuristics, scheduling, constraint satisfaction, optimization, configuration, and planning, and highlights the relationship between the search categories and the various ways a specific application can be modeled and solved using advanced problem solving techniques.

This book lends insight into solving some well-known AI problems using the most efficient problem-solving methods by humans and computers. The book discusses the importance of developing critical-thinking methods and skills, and develops a consistent approach toward each problem. This book assembles in one place a set of interesting and challenging AI–type problems that students regularly encounter in computer science, mathematics, and AI courses. These problems are not new, and students from all backgrounds can benefit from the kind of deductive thinking that goes into solving them. The book is especially useful as a companion to any course in computer science or mathematics where there are interesting problems to solve. Features: •Addresses AI and problem-solving from different perspectives •Covers classic AI problems such as Sudoku, Map Coloring, Twelve Coins, Red Donkey, Cryptarithms, Monte Carlo Methods, Rubik’s Cube, Missionaries/Cannibals, Knight’s Tour, Monty Hall, and more •Includes a companion disc with source code, solutions, figures, and more •Offers playability sites where students can exercise the process of developing their solutions •Describes problem-solving methods that might be applied to a variety of situations eBook Customers: Companion files are available for downloading with order number/proof of purchase by writing to the publisher at [email protected].

Thinking and Problem-Solving presents a comprehensive and up-to-date review of literature on cognition, reasoning, intelligence, and other formative areas specific to this field. Written for advanced undergraduates, researchers, and academics, this volume is a necessary reference for beginning and established investigators in cognitive and educational psychology. Thinking and Problem-Solving provides insight into questions such as: how do people solve complex problems in mathematics and everyday life? How do we generate new ideas? How do we piece together clues to solve a mystery, categorize novel events, and teach others to do the same? Provides a comprehensive literature review Covers both historical and contemporary approaches Organized for ease of use and reference Chapters authored by leading scholars

Design Problem Solving: Knowledge Structures and Control Strategies describes the application of the generic task methodology to the problem of routine design. This book discusses the generic task methodology and what constitutes the essence of the Al approach to problem solving, including the analysis of design as an information processing activity. The basic design problem solving framework, DSPL language, and AIR-CYL Air cylinder design system are also elaborated. Other topics include the high level languages based on generic tasks, structure of a Class 3 design problem solver, and failure handling in routine design. The conceptual structure for the air cylinder and improvements to DSPL system support are likewise covered in this text. This publication is beneficial to students and specialists concerned with solving design problems.

Machine learning is an emerging area of computer science that deals with the design and development of new algorithms based on various types of data. Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques addresses the complex realm of machine learning and its applications for solving various real-world problems in a variety of disciplines, such as manufacturing, business, information retrieval, and security. This premier reference source is essential for professors, researchers, and students in artificial intelligence as well as computer science and engineering.

Problem-solving strartegies and the nature of Heuristic informatio n.Heuristics and problem representations. Basic Heuristic-Search procedures. Formal properties of Heuristic methods. Heuristics viewed as information provided by simplified models. Performance analysis of Heuristic methods. Abstract models for quantitative performace analysis. Complexity versus precision of admissible Heuristics. Searching with nonadmissible Heuristics. Game-playing programs. Strategies and models for game-playing programs. Performace analysis for game-searching strategies. Decision quality in game searching. Bibliography. Index.

This book shows how collective intelligence combined with new technologies can help us solve the world's biggest problems.