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Due to the increasing importance of product differentiation and collapsing product life cycles, a growing number of value-adding activities in the industry and service sector are organized in projects. Projects come in many forms, often taking considerable time and consuming a large amount of resources. The management and scheduling of projects represents a challenging task and project performance may have a considerable impact on an organization's competitiveness. This handbook presents state-of-the-art approaches to project management and scheduling. More than sixty contributions written by leading experts in the field provide an authoritative survey of recent developments. The book serves as a comprehensive reference, both, for researchers and project management professionals. The handbook consists of two volumes. Volume 1 is devoted to single-modal and multi-modal project scheduling. Volume 2 presents multi-project problems, project scheduling under uncertainty and vagueness, managerial approaches and a separate part on applications, case studies and information systems.
First published in 2004. Routledge is an imprint of Taylor & Francis, an informa company.
Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.
A blind mage teams up with an unlikely ally to save a friend and the world in this quasi-Renaissance epic fantasy adventure by the author of The Soul Mirror. Indicted for crimes against the living and the dead, Dante the necromancer has become the most hated man in Sabria. Becoming blind by his enemy’s cruel vengeance only exacerbates his situation. These days, his only comfort is time spent with his student, Anne de Vernase, passing his knowledge on to her. But when her family greatly needs her, she must leave Dante. Then a retired soldier, haunted by powerful dreams, seeks out Dante’s help. Seeing a magical puzzle to solve and a chance to redeem himself, Dante offers his services—even though he senses the man’s plea hides something far more sinister. Soon the blind mage embarks on a mad journey with an unlikely ally beside him. Together they must rescue a former companion from a hellish demise that could raise a destructive cataclysm greater than any war their world has ever seen . . . “An amazingly complex and rewarding story, The Daemon Prince is certain to reward the devoted students of the Collegia Magica trilogy.” —Booklist “Enthralling and not to be missed.” —Kirkus Reviews “This rousing and complex good-against-evil battle concludes Berg’s voluminous quasi-Renaissance epic fantasy trilogy. . . . [Berg’s] insight into the nature of human good and evil, the constantly ebbing and flowing relationships among lovers and friends . . . consistently raises this novel above sword-and-sorcery routine.” —Publishers Weekly “Filled with action and feeling as if it occurs in a Berg version of the Age of Reason; fans will appreciate this stupendous story.” —Alternative Worlds