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Automated planning technology plays a significant role in a variety of demanding applications, ranging from controlling space vehicles and robots to playing the game of bridge. This book covers both the theory and practice of automated planning. It is useful for researchers, professionals, and graduate students.
Using intelligent agents as central characters, this work begins with elementary reactive agents. It reveals neural networks, genetic programming, computer vision, heuristic search, knowledge representation and reasoning, Bayes networks, and planning through the capabilities of these agents. It also illustrates the most important ideas in AI.
Constraint satisfaction is a simple but powerful tool. Constraint problems are used to model cognitive tasks in vision, language comprehension, default reasoning, diagnosis, scheduling, temporal and spatial reasoning. This title provides an examination of the theory that underlies constraint processing algorithms.
Case-based reasoning (CBR) is an intelligent-systems method that enables information managers to increase efficiency and reduce cost by substantially automating processes such as diagnosis, scheduling and design. This book explains the principles of CBR by describing its origins and contrasting it with familiar information disciplines.
Based on the metaphor of evolution, a genetic algorithm searches the information in a task and seeks the optimum solution by replacing weaker populations with stronger ones. This book is part of a series which records the Foundations of Genetic Algorithms Workshops, which addresses the publications on genetic algorithms and classifier systems.
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