The Intelligent Techniques for Planning presents a number of modern approaches to the area of automated planning. These approaches combine methods from classical planning such as the construction of graphs and the use of domain-independent heuristics with techniques from other areas of artificial intelligence. This book discuses, in detail, a number of state-of-the-art planning systems that utilize constraint satisfaction techniques in order to deal with time and resources, machine learning in order to utilize experience drawn from past runs, methods from knowledge systems for more expressive representation of knowledge and ideas from other areas such as Intelligent Agents. Apart from the thorough analysis and implementation details, each chapter of the book also provides extensive background information about its subject and presents and comments on similar approaches done in the past.

This book presents recently developed intelligent techniques with applications and theory in the area of engineering management. The involved applications of intelligent techniques such as neural networks, fuzzy sets, Tabu search, genetic algorithms, etc. will be useful for engineering managers, postgraduate students, researchers, and lecturers. The book has been written considering the contents of a classical engineering management book but intelligent techniques are used for handling the engineering management problem areas. This comprehensive characteristics of the book makes it an excellent reference for the solution of complex problems of engineering management. The authors of the chapters are well-known researchers with their previous works in the area of engineering management.

"The central fact is that we are planning agents." (M. Bratman, Intentions, Plans, and Practical Reasoning, 1987, p. 2) Recent arguments to the contrary notwithstanding, it seems to be the case that people-the best exemplars of general intelligence that we have to date do a lot of planning. It is therefore not surprising that modeling the planning process has always been a central part of the Artificial Intelligence enterprise. Reasonable behavior in complex environments requires the ability to consider what actions one should take, in order to achieve (some of) what one wants and that, in a nutshell, is what AI planning systems attempt to do. Indeed, the basic description of a plan generation algorithm has remained constant for nearly three decades: given a desciption of an initial state I, a goal state G, and a set of action types, find a sequence S of instantiated actions such that when S is executed instate I, G is guaranteed as a result. Working out the details of this class of algorithms, and making the elabora tions necessary for them to be effective in real environments, have proven to be bigger tasks than one might have imagined.

This book presents recently developed intelligent techniques with applications and theory in the area of quality management. The involved applications of intelligence include techniques such as fuzzy sets, neural networks, genetic algorithms, etc. The book consists of classical quality management topics dealing with intelligent techniques for solving the complex quality management problems. The book will serve as an excellent reference for quality managers, researchers, lecturers and postgraduate students in this area. The authors of the chapters are well-known researchers in the area of quality management.

Robotics is an ever-expanding field and intelligent planning continues to play a major role. Given that the intention of mobile robots is to carry out tasks independent from human aid, robot intelligence is needed to make and plan out decisions based on various sensors. Planning is the fundamental activity that implements this intelligence into the mobile robots to complete such tasks. Understanding problems, challenges, and solutions to path planning and how it fits in is important to the realm of robotics. Intelligent Planning for Mobile Robotics: Algorithmic Approaches presents content coverage on the basics of artificial intelligence, search problems, and soft computing approaches. This collection of research provides insight on both robotics and basic algorithms and could serve as a reference book for courses related to robotics, special topics in AI, planning, applied soft computing, applied AI, and applied evolutionary computing. It is an ideal choice for research students, scholars, and professors alike.

Effective utilization of equipment is critical to any manufacturing operation, especially with today's sophisticated, high-cost equipment and increased global competition. To meet these challenges in the manufacturing industry, you must understand and implement the myriad conventional and intelligent techniques for different types of manufacturing problems. Manufacturing Optimization Through Intelligent Techniques covers design of machine elements, integrated product development, machining tolerance allocation, selection of operating parameters for CNC machine tools, scheduling, part family formation, selection of robot coordinates, robot trajectory planning and both conventional and intelligent techniques, providing the tools to design and implement a suitable optimization technique. The author explores how to model optimization problems, select suitable techniques, develop the optimization algorithm and software, and implement the program. The book delineates five new techniques using examples taken from the literature for optimization problems in design, tolerance allocation; selection of machining parameters, integrated product development, scheduling, concurrent formation of machine groups and part families, selection of robot co-ordinates, robot trajectory planning and intelligent machining. All the manufacturing functions described have been successfully solved by Genetic Algorithm. Other intelligent techniques have been implemented only for solving certain types of problems: simulated annealing; design and scheduling, particle swarm optimization and ant colony optimization; tolerance allocation and tabu search; as well as machining parameters optimization. After reading this book, you will understand the different types of manufacturing optimization problems as well as the conventional and intelligent techniques suitable for solving them. You will also be able to develop and implement effective optimization procedures and algorithms for a wide variety of problems in design manufacturing.

This book offers a comprehensive reference guide to intelligence systems in environmental management. It provides readers with all the necessary tools for solving complex environmental problems, where classical techniques cannot be applied. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts including ant colony, genetic algorithms, evolutionary algorithms, fuzzy multi-criteria decision making tools, particle swarm optimization, agent-based modelling, artificial neural networks, simulated annealing, Tabu search, fuzzy multi-objective optimization, fuzzy rules, support vector machines, fuzzy cognitive maps, cumulative belief degrees, and many others. To foster a better understanding, all the chapters include relevant numerical examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on complex environmental problems. Moreover, by extending all the main aspects of classical environmental solution techniques to its intelligent counterpart, the book presents a dynamic snapshot on the field that is expected to stimulate new directions and stimulate new ideas and developments.

The Intelligent Techniques for Planning presents a number of modern approaches to the area of automated planning. These approaches combine methods from classical planning such as the construction of graphs and the use of domain-independent heuristics with techniques from other areas of artificial intelligence. This book discuses, in detail, a number of state-of-the-art planning systems that utilize constraint satisfaction techniques in order to deal with time and resources, machine learning in order to utilize experience drawn from past runs, methods from knowledge systems for more expressive representation of knowledge and ideas from other areas such as Intelligent Agents. Apart from the thorough analysis and implementation details, each chapter of the book also provides extensive background information about its subject and presents and comments on similar approaches done in the past.

Solving complex problems in real-world contexts, such as financial investment planning or mining large data collections, involves many different sub-tasks, each of which requires different techniques. To deal with such problems, a great diversity of intelligent techniques are available, including traditional techniques like expert systems approaches and soft computing techniques like fuzzy logic, neural networks, or genetic algorithms. These techniques are complementary approaches to intelligent information processing rather than competing ones, and thus better results in problem solving are achieved when these techniques are combined in hybrid intelligent systems. Multi-Agent Systems are ideally suited to model the manifold interactions among the many different components of hybrid intelligent systems. This book introduces agent-based hybrid intelligent systems and presents a framework and methodology allowing for the development of such systems for real-world applications. The authors focus on applications in financial investment planning and data mining.

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