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Principles of noology[electronic resource] :toward a theory and science of intelligence /

纪录类型:	书目-电子资源 : Monograph/item
[NT 15000414] null:	006.3
[NT 47271] Title/Author:	Principles of noology : toward a theory and science of intelligence // by Seng-Beng Ho.
作者:	Ho, Seng-Beng.
出版者:	Cham : : Springer International Publishing :, 2016.
面页册数:	xix, 431 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
标题:	Artificial intelligence.
标题:	Computational neuroscience.
标题:	Medicine.
标题:	Science.
标题:	Neurosciences.
标题:	Computational intelligence.
标题:	Biomedicine.
标题:	Artificial Intelligence (incl. Robotics)
标题:	Computational Intelligence.
标题:	Science, general.
ISBN:	9783319321134
ISBN:	9783319321110
[NT 15000228] null:	Preface -- Acknowledgement -- Introduction -- Rapid Unsupervised Effective Causal Learning -- A General Noological Framework -- Conceptual Grounding and Operational Representation -- Causal Rules, Problem Solving, and Operational Representation -- The Causal Role of Sensory Information -- Application to the StarCraft Game Environment -- A Grand Challenge for Noology and Computational Intelligence -- Affect Driven Noological Processes -- Summary and Beyond -- Appendix A: Causal vs Reinforcement Learning -- Appendix B: Rapid Effective Causal Learning Algorithm -- Index.
[NT 15000229] null:	The idea of this book is to establish a new scientific discipline, "noology," under which a set of fundamental principles are proposed for the characterization of both naturally occurring and artificial intelligent systems. The methodology adopted in Principles of Noology for the characterization of intelligent systems, or "noological systems," is a computational one, much like that of AI. Many AI devices such as predicate logic representations, search mechanisms, heuristics, and computational learning mechanisms are employed but they are recast in a totally new framework for the characterization of noological systems. The computational approach in this book provides a quantitative and high resolution understanding of noological processes, and at the same time the principles and methodologies formulated are directly implementable in AI systems. In contrast to traditional AI that ignores motivational and affective processes, under the paradigm of noology, motivational and affective processes are central to the functioning of noological systems and their roles in noological processes are elucidated in detailed computational terms. In addition, a number of novel representational and learning mechanisms are proposed, and ample examples and computer simulations are provided to show their applications. These include rapid effective causal learning (a novel learning mechanism that allows an AI/noological system to learn causality with a small number of training instances), learning of scripts that enables knowledge chunking and rapid problem solving, and learning of heuristics that further accelerates problem solving. Semantic grounding allows an AI/noological system to "truly understand" the meaning of the knowledge it encodes. This issue is extensively explored. This is a highly informative book providing novel and deep insights into intelligent systems which is particularly relevant to both researchers and students of AI and the cognitive sciences.
电子资源:	http://dx.doi.org/10.1007/978-3-319-32113-4

Principles of noology[electronic resource] :toward a theory and science of intelligence /
Ho, Seng-Beng.

Principles of noologytoward a theory and science of intelligence /[electronic resource] :by Seng-Beng Ho. - Cham :Springer International Publishing :2016. - xix, 431 p. :ill., digital ;24 cm. - Socio-affective computing,v.32509-5706 ;. - Socio-affective computing ;v.1..

Preface -- Acknowledgement -- Introduction -- Rapid Unsupervised Effective Causal Learning -- A General Noological Framework -- Conceptual Grounding and Operational Representation -- Causal Rules, Problem Solving, and Operational Representation -- The Causal Role of Sensory Information -- Application to the StarCraft Game Environment -- A Grand Challenge for Noology and Computational Intelligence -- Affect Driven Noological Processes -- Summary and Beyond -- Appendix A: Causal vs Reinforcement Learning -- Appendix B: Rapid Effective Causal Learning Algorithm -- Index.

The idea of this book is to establish a new scientific discipline, "noology," under which a set of fundamental principles are proposed for the characterization of both naturally occurring and artificial intelligent systems. The methodology adopted in Principles of Noology for the characterization of intelligent systems, or "noological systems," is a computational one, much like that of AI. Many AI devices such as predicate logic representations, search mechanisms, heuristics, and computational learning mechanisms are employed but they are recast in a totally new framework for the characterization of noological systems. The computational approach in this book provides a quantitative and high resolution understanding of noological processes, and at the same time the principles and methodologies formulated are directly implementable in AI systems. In contrast to traditional AI that ignores motivational and affective processes, under the paradigm of noology, motivational and affective processes are central to the functioning of noological systems and their roles in noological processes are elucidated in detailed computational terms. In addition, a number of novel representational and learning mechanisms are proposed, and ample examples and computer simulations are provided to show their applications. These include rapid effective causal learning (a novel learning mechanism that allows an AI/noological system to learn causality with a small number of training instances), learning of scripts that enables knowledge chunking and rapid problem solving, and learning of heuristics that further accelerates problem solving. Semantic grounding allows an AI/noological system to "truly understand" the meaning of the knowledge it encodes. This issue is extensively explored. This is a highly informative book providing novel and deep insights into intelligent systems which is particularly relevant to both researchers and students of AI and the cognitive sciences.

ISBN: 9783319321134

Standard No.: 10.1007/978-3-319-32113-4doiSubjects--Topical Terms:

172060
Artificial intelligence.

LC Class. No.: Q335

Dewey Class. No.: 006.3

Principles of noology[electronic resource] :toward a theory and science of intelligence /
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245 1 0 $a Principles of noology $h [electronic resource] : $b toward a theory and science of intelligence / $c by Seng-Beng Ho.
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505 0 $a Preface -- Acknowledgement -- Introduction -- Rapid Unsupervised Effective Causal Learning -- A General Noological Framework -- Conceptual Grounding and Operational Representation -- Causal Rules, Problem Solving, and Operational Representation -- The Causal Role of Sensory Information -- Application to the StarCraft Game Environment -- A Grand Challenge for Noology and Computational Intelligence -- Affect Driven Noological Processes -- Summary and Beyond -- Appendix A: Causal vs Reinforcement Learning -- Appendix B: Rapid Effective Causal Learning Algorithm -- Index.
520 $a The idea of this book is to establish a new scientific discipline, "noology," under which a set of fundamental principles are proposed for the characterization of both naturally occurring and artificial intelligent systems. The methodology adopted in Principles of Noology for the characterization of intelligent systems, or "noological systems," is a computational one, much like that of AI. Many AI devices such as predicate logic representations, search mechanisms, heuristics, and computational learning mechanisms are employed but they are recast in a totally new framework for the characterization of noological systems. The computational approach in this book provides a quantitative and high resolution understanding of noological processes, and at the same time the principles and methodologies formulated are directly implementable in AI systems. In contrast to traditional AI that ignores motivational and affective processes, under the paradigm of noology, motivational and affective processes are central to the functioning of noological systems and their roles in noological processes are elucidated in detailed computational terms. In addition, a number of novel representational and learning mechanisms are proposed, and ample examples and computer simulations are provided to show their applications. These include rapid effective causal learning (a novel learning mechanism that allows an AI/noological system to learn causality with a small number of training instances), learning of scripts that enables knowledge chunking and rapid problem solving, and learning of heuristics that further accelerates problem solving. Semantic grounding allows an AI/noological system to "truly understand" the meaning of the knowledge it encodes. This issue is extensively explored. This is a highly informative book providing novel and deep insights into intelligent systems which is particularly relevant to both researchers and students of AI and the cognitive sciences.
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650 2 4 $a Computational Intelligence. $3 463962
650 2 4 $a Science, general. $3 464008
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