000			04297nam a22004215i 4500
001			85879
003			ES-MaUEC
005			20230207040532.0
007			cr nn 008mamaa
008			160628s2016 gw | s |||| 0|eng d
020			_a9783319321134
040			_aES-MaUEC
050		4	_aQ335 _b.H674 2016 EB
082	0	4	_a612.8
090			_aQ335 _b.H674 2016
100	1		_aHo, Seng-Beng _0Local _999163
245	1	0	_aPrinciples of Noology : _bToward a Theory and Science of Intelligence _cby Seng-Beng Ho
260			_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XIX, 431 p.) _b241 ilustraciones, 220 ilustraciones en color
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	1		_aSocio-Affective Computing _x2509-5706 _v3
505	0		_aPreface -- 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			_aThe idea of this book is to establish a new scientific discipline, znoology,y 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 znoological systems,y 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 ztruly understandy 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.
710	2		_aSpringerLink (Online service) _0Local _9106996
942			_2lcc _cLE
988	0	0	_aEBOOK, EBSPRINGER
650		7	_aMedicina _0comprobar BNE19900959047 _2embne _9405021
650		7	_aCiencia _0comprobar BNE19900974044 _2embne _9139568
650		7	_aNeurociencias _0comprobar BNE20012490612 _2embne _9158907
830		0	_aSocio-Affective Computing _x2509-5706 _v3 _9133836
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-32113-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319321134
907			_a.b12951572 _b10-10-17 _c21-11-16
998			_am _a_alco _a_vill _b - - _cm _dz _e- _feng _ggw _h0
945			_aQ335 .H674 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11614602 _z30-06-17
999			_c85879 _d85879 _x1