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.

"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.

Discusses generic planning problems with robotics-specific considerations. Includes the recent results in reconfigurable robot planning, multiple robot planning, plan recovery, and planning in uncertain environments.

This book presents the most recent and advanced techniques for creating autonomous AI systems capable of planning and acting effectively.

Publisher Description

The first comparative examination of planning paradigms This text begins with the principle that the ability to anticipateand plan is an essential feature of intelligent systems, whetherhuman or machine. It further assumes that better planning resultsin greater achievements. With these principles as a foundation,Planning in Intelligent Systems provides readers with the toolsneeded to better understand the process of planning and to becomebetter planners themselves. The text is divided into two parts: * Part One, "Theoretical," discusses the predominant schools ofthought in planning: psychology and cognitive science,organizational science, computer science, mathematics, artificialintelligence, and systems theory. In particular, the book examinescommonalities and differences among the goals, methods, andtechniques of these various approaches to planning. The result is abetter understanding of the process of planning through thecross-fertilization of ideas. Each chapter contains a shortintroduction that sets forth the interrelationships of that chapterto the main ideas featured in the other chapters. * Part Two, "Practical," features six chapters that center on acase study of The Netherlands Railways. Readers learn to applytheory to a real-world situation and discoverhow expanding theirrepertoire of planning methods can help solve seemingly intractableproblems. All chapters have been contributed by leading experts in thevarious schools of planning and carefully edited to ensure aconsistent high standard throughout. This book is designed to not only expand the range of planningtools used, but also to enable readers to use them moreeffectively. It challenges readers to look at new approaches andlearn from new schools of thought. Planning in Intelligent Systemsdelivers effective planning approaches for researchers, professors,students, and practitioners in artificial intelligence, computerscience, cognitive psychology, and mathematics, as well as industryplanners and managers.

Provides a concise introduction to the use of Markov Decision Processes for solving probabilistic planning problems, with an emphasis on the algorithmic perspective. It covers the whole spectrum of the field, from the basics to state-of-the-art optimal and approximation algorithms.

Planning, or reasoning about actions, is a fundamental element of intelligent behavior--and one that artificial intelligence has found very difficult to implement. The most well-understood approach to building planning systems has been under refinement since the late 1960s and has now reached a level of maturity where there are good prospects for building working planners. Practical Planning is an in-depth examination of this classical planning paradigm through an intensive case study of SIPE, a significantly implemented planning system. The author, the developer of SIPE, defines the planning problem in general, explains why reasoning about actions is so complex, and describes all parts of the SIPE system and the algorithms needed to achieve efficiency. Details are discussed in the context of problems and important issues in building a practical planner; discussions of how other systems address these issues are also included. Assuming only a basic background in AI, Practical Planning will be of great interest to professionals interested in incorporating planning capabilities into AI systems.

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. Written for computer scientists and engineers with interests in artificial intelligence, robotics, or control theory, this is the only book on this topic that tightly integrates a vast body of literature from several fields into a coherent source for teaching and reference in a wide variety of applications. Difficult mathematical material is explained through hundreds of examples and illustrations.

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.