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Author: Jonathan Matthew Gratch Publisher: ISBN: Category : Machine learning Languages : en Pages : 38
Book Description
These 'learning operators' define a space of possible transformations through which a system must search for a [sic] efficient planner. We show that the complexity of this search precludes a general solution and can only be approached via simplifications. We illustrate the frequently unarticulated commitments which underly current learning approaches. These simplifications improve learning efficiency but not without tradeoffs. In some cases these tradeoffs result in less than optimal behavior. In others, they produce planners which become worse through learning. It is hoped that by articulating these commitments we can better understand their ramifications.
Author: Jonathan Matthew Gratch Publisher: ISBN: Category : Machine learning Languages : en Pages : 38
Book Description
These 'learning operators' define a space of possible transformations through which a system must search for a [sic] efficient planner. We show that the complexity of this search precludes a general solution and can only be approached via simplifications. We illustrate the frequently unarticulated commitments which underly current learning approaches. These simplifications improve learning efficiency but not without tradeoffs. In some cases these tradeoffs result in less than optimal behavior. In others, they produce planners which become worse through learning. It is hoped that by articulating these commitments we can better understand their ramifications.
Author: Steven Minton Publisher: Morgan Kaufmann ISBN: 1483221172 Category : Social Science Languages : en Pages : 554
Book Description
Machine Learning Methods for Planning provides information pertinent to learning methods for planning and scheduling. This book covers a wide variety of learning methods and learning architectures, including analogical, case-based, decision-tree, explanation-based, and reinforcement learning. Organized into 15 chapters, this book begins with an overview of planning and scheduling and describes some representative learning systems that have been developed for these tasks. This text then describes a learning apprentice for calendar management. Other chapters consider the problem of temporal credit assignment and describe tractable classes of problems for which optimal plans can be derived. This book discusses as well how reactive, integrated systems give rise to new requirements and opportunities for machine learning. The final chapter deals with a method for learning problem decompositions, which is based on an idealized model of efficiency for problem-reduction search. This book is a valuable resource for production managers, planners, scientists, and research workers.
Author: Steven Michael LaValle Publisher: ISBN: 9780511241338 Category : Algorithms Languages : en Pages : 826
Book Description
Written for computer scientists and engineers with interests in artificial intelligence, robotics, or control theory, this is the only book on this topic that integrates literature from several fields into a coherent source for teaching and reference in applications including robotics, computational biology, computer graphics, manufacturing, aerospace applications, and medicine.
Author: Ioannis Vlahavas Publisher: IGI Global ISBN: 1591404525 Category : Computers Languages : en Pages : 364
Book Description
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.
Author: Nikolaos G. Bourbakis Publisher: World Scientific ISBN: 9789810205461 Category : Computers Languages : en Pages : 406
Book Description
Learning and planning are two important topics of artificial intelligence. Learning deals with the algorithmic processes that make a computing machine able to ?learn? and improve its performance during the process of complex tasks. Planning on the other hand, deals with decision and construction processes that make a machine capable of constructing an intelligent plan for the solution of a particular complex problem.This book combines both learning and planning methodologies and their applications in different domains. It is divided into two parts. The first part contains seven chapters on the ongoing research work in symbolic and connectionist learning. The second part includes seven chapters which provide the current research efforts in planning methodologies and their application to robotics.
Author: James Hendler Publisher: Elsevier ISBN: 0080499449 Category : Computers Languages : en Pages : 315
Book Description
Artificial Intelligence Planning Systems documents the proceedings of the First International Conference on AI Planning Systems held in College Park, Maryland on June 15-17, 1992. This book discusses the abstract probabilistic modeling of action; building symbolic primitives with continuous control routines; and systematic adaptation for case-based planning. The analysis of ABSTRIPS; conditional nonlinear planning; and building plans to monitor and exploit open-loop and closed-loop dynamics are also elaborated. This text likewise covers the modular utility representation for decision-theoretic planning; reaction and reflection in tetris; and planning in intelligent sensor fusion. Other topics include the resource-bounded adaptive agent, critical look at Knoblock's hierarchy mechanism, and traffic laws for mobile robots. This publication is beneficial to students and researchers conducting work on AI planning systems.
Author: Jonathan Matthew Gratch
Author: Jonathan Matthew Gratch
Author: Steven Minton
Author: 
Author: Steven Michael LaValle
Author: Machine Learning
Author: D. Sleeman
Author: Ioannis Vlahavas
Author: Nikolaos G. Bourbakis
Author: James Hendler
Author: Machine Learning