Andere Kunden interessierten sich auch für
![Representing and Reasoning with Qualitative Preferences]( "Representing and Reasoning with Qualitative Preferences")
Representing and Reasoning with Qualitative Preferences42,79 €![Planning with Markov Decision Processes]( "Planning with Markov Decision Processes")
Planning with Markov Decision Processes37,44 €![An Introduction to Constraint-Based Temporal Reasoning]( "An Introduction to Constraint-Based Temporal Reasoning")
An Introduction to Constraint-Based Temporal Reasoning35,30 €![A Concise Introduction to Models and Methods for Automated Planning]( "A Concise Introduction to Models and Methods for Automated Planning")
A Concise Introduction to Models and Methods for Automated Planning35,30 €![Case-Based Reasoning]( "Case-Based Reasoning")
Case-Based Reasoning29,95 €![Learning with Support Vector Machines]( "Learning with Support Vector Machines")
Learning with Support Vector Machines29,95 €![Applying Reinforcement Learning on Real-World Data with Practical Examples in Python]( "Applying Reinforcement Learning on Real-World Data with Practical Examples in Python")
Applying Reinforcement Learning on Real-World Data with Practical Examples in Python58,84 €
Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well known that the tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques that significantly advanced the state of the art.
This book provides comprehensive coverage of the primary exact algorithms for reasoning with such models. The main feature exploited by the algorithms is the model's graph. We present inference-based, message-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search). Each class possesses distinguished characteristics and in particular has different time vs. space behavior. We emphasize the dependence of both schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. The new edition includes the notion of influence diagrams, which focus on sequential decision making under uncertainty. We believe the principles outlined in the book would serve well in moving forward to approximation and anytime-based schemes. The target audience of this book is researchers and students in the artificial intelligence and machine learning area, and beyond.
This book provides comprehensive coverage of the primary exact algorithms for reasoning with such models. The main feature exploited by the algorithms is the model's graph. We present inference-based, message-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search). Each class possesses distinguished characteristics and in particular has different time vs. space behavior. We emphasize the dependence of both schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. The new edition includes the notion of influence diagrams, which focus on sequential decision making under uncertainty. We believe the principles outlined in the book would serve well in moving forward to approximation and anytime-based schemes. The target audience of this book is researchers and students in the artificial intelligence and machine learning area, and beyond.