Robot Learning [electronic resource] /

Robot Learning [electronic resource] /

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Building a robot that learns to perform a task has been acknowledged as one of the major challenges facing artificial intelligence. Self-improving robots would relieve humans from much of the drudgery of programming and would potentially allow operation in environments that were changeable or only partially known. Progress towards this goal would also make fundamental contributions to artificial intelligence by furthering our understanding of how to successfully integrate disparate abilities such as perception, planning, learning and action. Although its roots can be traced back to the late fifties, the area of robot learning has lately seen a resurgence of interest. The flurry of interest in robot learning has partly been fueled by exciting new work in the areas of reinforcement earning, behavior-based architectures, genetic algorithms, neural networks and the study of artificial life. Robot Learning gives an overview of some of the current research projects in robot learning being carried out at leading universities and research laboratories in the United States. The main research directions in robot learning covered in this book include: reinforcement learning, behavior-based architectures, neural networks, map learning, action models, navigation and guided exploration.

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Bibliographic Details
Main Authors: Connell, Jonathan H. editor., Mahadevan, Sridhar. editor., SpringerLink (Online service)
Format: Texto biblioteca
Language:eng
Published: Boston, MA : Springer US : Imprint: Springer, 1993
Subjects:Engineering., Artificial intelligence., Control engineering., Robotics., Mechatronics., Automation., Robotics and Automation., Control, Robotics, Mechatronics., Artificial Intelligence (incl. Robotics).,
Online Access:http://dx.doi.org/10.1007/978-1-4615-3184-5
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id KOHA-OAI-TEST:193696
record_format koha
institution COLPOS
collection Koha
country México
countrycode MX
component Bibliográfico
access En linea
En linea
databasecode cat-colpos
tag biblioteca
region America del Norte
libraryname Departamento de documentación y biblioteca de COLPOS
language eng
topic Engineering.
Artificial intelligence.
Control engineering.
Robotics.
Mechatronics.
Automation.
Engineering.
Robotics and Automation.
Control, Robotics, Mechatronics.
Artificial Intelligence (incl. Robotics).
Engineering.
Artificial intelligence.
Control engineering.
Robotics.
Mechatronics.
Automation.
Engineering.
Robotics and Automation.
Control, Robotics, Mechatronics.
Artificial Intelligence (incl. Robotics).
spellingShingle Engineering.
Artificial intelligence.
Control engineering.
Robotics.
Mechatronics.
Automation.
Engineering.
Robotics and Automation.
Control, Robotics, Mechatronics.
Artificial Intelligence (incl. Robotics).
Engineering.
Artificial intelligence.
Control engineering.
Robotics.
Mechatronics.
Automation.
Engineering.
Robotics and Automation.
Control, Robotics, Mechatronics.
Artificial Intelligence (incl. Robotics).
Connell, Jonathan H. editor.
Mahadevan, Sridhar. editor.
SpringerLink (Online service)
Robot Learning [electronic resource] /
description Building a robot that learns to perform a task has been acknowledged as one of the major challenges facing artificial intelligence. Self-improving robots would relieve humans from much of the drudgery of programming and would potentially allow operation in environments that were changeable or only partially known. Progress towards this goal would also make fundamental contributions to artificial intelligence by furthering our understanding of how to successfully integrate disparate abilities such as perception, planning, learning and action. Although its roots can be traced back to the late fifties, the area of robot learning has lately seen a resurgence of interest. The flurry of interest in robot learning has partly been fueled by exciting new work in the areas of reinforcement earning, behavior-based architectures, genetic algorithms, neural networks and the study of artificial life. Robot Learning gives an overview of some of the current research projects in robot learning being carried out at leading universities and research laboratories in the United States. The main research directions in robot learning covered in this book include: reinforcement learning, behavior-based architectures, neural networks, map learning, action models, navigation and guided exploration.
format Texto
topic_facet Engineering.
Artificial intelligence.
Control engineering.
Robotics.
Mechatronics.
Automation.
Engineering.
Robotics and Automation.
Control, Robotics, Mechatronics.
Artificial Intelligence (incl. Robotics).
author Connell, Jonathan H. editor.
Mahadevan, Sridhar. editor.
SpringerLink (Online service)
author_facet Connell, Jonathan H. editor.
Mahadevan, Sridhar. editor.
SpringerLink (Online service)
author_sort Connell, Jonathan H. editor.
title Robot Learning [electronic resource] /
title_short Robot Learning [electronic resource] /
title_full Robot Learning [electronic resource] /
title_fullStr Robot Learning [electronic resource] /
title_full_unstemmed Robot Learning [electronic resource] /
title_sort robot learning [electronic resource] /
publisher Boston, MA : Springer US : Imprint: Springer,
publishDate 1993
url http://dx.doi.org/10.1007/978-1-4615-3184-5
work_keys_str_mv AT connelljonathanheditor robotlearningelectronicresource
AT mahadevansridhareditor robotlearningelectronicresource
AT springerlinkonlineservice robotlearningelectronicresource
_version_ 1756266503708082176
spelling KOHA-OAI-TEST:1936962018-07-30T23:18:52ZRobot Learning [electronic resource] / Connell, Jonathan H. editor. Mahadevan, Sridhar. editor. SpringerLink (Online service) textBoston, MA : Springer US : Imprint: Springer,1993.engBuilding a robot that learns to perform a task has been acknowledged as one of the major challenges facing artificial intelligence. Self-improving robots would relieve humans from much of the drudgery of programming and would potentially allow operation in environments that were changeable or only partially known. Progress towards this goal would also make fundamental contributions to artificial intelligence by furthering our understanding of how to successfully integrate disparate abilities such as perception, planning, learning and action. Although its roots can be traced back to the late fifties, the area of robot learning has lately seen a resurgence of interest. The flurry of interest in robot learning has partly been fueled by exciting new work in the areas of reinforcement earning, behavior-based architectures, genetic algorithms, neural networks and the study of artificial life. Robot Learning gives an overview of some of the current research projects in robot learning being carried out at leading universities and research laboratories in the United States. The main research directions in robot learning covered in this book include: reinforcement learning, behavior-based architectures, neural networks, map learning, action models, navigation and guided exploration.1 Introduction to Robot Learning -- 1 Motivation -- 2 The Robot Learning Problem -- 3 Background -- 4 Domains -- 5 Roadmap -- 2 Knowledge-based Training of Artificial Neural Networks for Autonomous Robot Driving -- 1 Introduction -- 2 Network Architecture -- 3 Network Training -- 4 Performance Improvement Using Transformations -- 5 Results and Comparison -- 6 Discussion -- 3 Learning Multiple Goal Behavior via Task Decomposition and Dynamic Policy Merging -- 1 Introduction -- 2 Basics of Reinforcement Learning -- 3 Multiple Goal Tasks -- 4 A Decomposition Approach -- 5 Search-based Merging -- 6 A Hybrid Architecture -- 7 Summary -- 4 Memory-based Reinforcement Learning:Converging with Less Data and Less Real Time -- 1 Introduction -- 2 Prioritized Sweeping -- 3 A Markov Prediction Experiment -- 4 Learning Control of Markov Decision Tasks -- 5 Experimental Results -- 6 Discussion -- 7 Conclusion -- 5 Rapid Task Learning for Real Robots -- 1 Introduction -- 2 Behavior-based Reinforcement Learning -- 3 Exploiting Local Spatial Structure -- 4 Using Action Models -- 5 Highly Structured Learning -- 6 Summary -- 6 The Semantic Hierarchy in Robot Learning -- 1 Introduction -- 2 The Cognitive Map and the Semantic Hierarchy -- 3 From Simulated Robot to Physical Robots -- 4 From Tabula Rasa to Cognitive Mapping -- 5 From Low-Speed to High-Speed Motion -- 6 Conclusions -- 7 Uncertainty In Graph-Based Map Learning -- 1 Introduction -- 2 Qualitative Navigation and Map Learning -- 3 Theoretical Development -- 4 Problem Classification -- 5 Summary of Results -- 6 Conclusions -- 8 Real Robots, Real Learning Problems -- 1 Introduction -- 2 Motivation -- 3 The Main Types of Learning -- 4 The Main Methods of Learning -- 5 Simulation -- 6 Conclusion.Building a robot that learns to perform a task has been acknowledged as one of the major challenges facing artificial intelligence. Self-improving robots would relieve humans from much of the drudgery of programming and would potentially allow operation in environments that were changeable or only partially known. Progress towards this goal would also make fundamental contributions to artificial intelligence by furthering our understanding of how to successfully integrate disparate abilities such as perception, planning, learning and action. Although its roots can be traced back to the late fifties, the area of robot learning has lately seen a resurgence of interest. The flurry of interest in robot learning has partly been fueled by exciting new work in the areas of reinforcement earning, behavior-based architectures, genetic algorithms, neural networks and the study of artificial life. Robot Learning gives an overview of some of the current research projects in robot learning being carried out at leading universities and research laboratories in the United States. The main research directions in robot learning covered in this book include: reinforcement learning, behavior-based architectures, neural networks, map learning, action models, navigation and guided exploration.Engineering.Artificial intelligence.Control engineering.Robotics.Mechatronics.Automation.Engineering.Robotics and Automation.Control, Robotics, Mechatronics.Artificial Intelligence (incl. Robotics).Springer eBookshttp://dx.doi.org/10.1007/978-1-4615-3184-5URN:ISBN:9781461531845

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