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Planning with Markov Decision Processes
Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its actions could have uncertain outcomes. MDPs are actively researched in two related subareas of AI, probabilistic planning and reinforcement learning. Probabilistic planning assumes known models for the agent's goals and domain dynamics, and focuses on determining how the agent should behave to achieve its objectives. On the other hand, reinforcement learning additionally learns these models based on...
Junk Jet n°1
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Recent Advances in Constraints
This book constitutes the thoroughly refereed and extended post-workshop proceedings of the 13th Annual ERCIM International Workshop on Constraint Solving and Constraint Logic Programming, CSCLP 2008, held in Rome, Italy, in June 2008. The 9 revised full papers presented were carefully reviewed and selected from 14 initial submissions. The papers in this volume present original research results, as well as applications, in many aspects of constraint solving and constraint logic programming. Research topics that can be found in the papers are ̄rst-order constraints, symmetry breaking, global constraints, constraint optimization problems, distributed constraint solving problems, soft constraints, as well as the analysis of application domains such as cumulative resource problems and hybrid systems.
Principles of Knowledge Representation and Reasoning
The proceedings of KR '94 comprise 55 papers on topics including deduction an search, description logics, theories of knowledge and belief, nonmonotonic reasoning and belief revision, action and time, planning and decision-making and reasoning about the physical world, and the relations between KR
Lifting the Covers
Luke Alfred lifts the covers on South African cricket and its struggle to reinvent itself after years in the international wilderness. The author presents the facts, as exposed to public scrutiny, from an insider's perspective.
Advanced Path Planning for Mobile Entities
The book Advanced Path Planning for Mobile Entities provides a platform for practicing researchers, academics, PhD students, and other scientists to design, analyze, evaluate, process, and implement diversiform issues of path planning, including algorithms for multipath and mobile planning and path planning for mobile robots. The nine chapters of the book demonstrate capabilities of advanced path planning for mobile entities to solve scientific and engineering problems with varied degree of complexity.
Intelligent Autonomous Systems 9
Autonomy and adaptivity are key aspects of truly intelligent artificial systems, dating from the first IAS conference in 1989. The goal of IAS-9 is to lay out scientific ideas and design principles for artificial systems. This work contains papers that cover both the applied and the theoretical aspects of intelligent autonomous systems.
Business Process Management
This book constitutes the refereed proceedings of the 4th International Conference on Business Process Management, BPM 2006. The book presents 20 revised full papers, 5 industrial papers, and 15 short papers together with an invited paper and the abstract of an invited talk. The papers are organized in topical sections on monitoring and mining, service composition, process models and languages, dynamic process management, Web service composition, and applied business process management.
Recent Advances in Reinforcement Learning
Recent Advances in Reinforcement Learning addresses current research in an exciting area that is gaining a great deal of popularity in the Artificial Intelligence and Neural Network communities. Reinforcement learning has become a primary paradigm of machine learning. It applies to problems in which an agent (such as a robot, a process controller, or an information-retrieval engine) has to learn how to behave given only information about the success of its current actions. This book is a collection of important papers that address topics including the theoretical foundations of dynamic programming approaches, the role of prior knowledge, and methods for improving performance of reinforcement-learning techniques. These papers build on previous work and will form an important resource for students and researchers in the area. Recent Advances in Reinforcement Learning is an edited volume of peer-reviewed original research comprising twelve invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 22, Numbers 1, 2 and 3).
Advances in Neural Information Processing Systems
The proceedings of the 2001 Neural Information Processing Systems (NIPS) Conference. The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. The conference is interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing, reinforcement learning and control, implementations, and diverse applications. Only about 30 percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. These proceedings contain all of the papers that were presented at the 2001 conference.
