000			04352nam a22005175i 4500
001			978-3-319-32113-4
003			DE-He213
005			20180206183139.0
007			cr nn 008mamaa
008			160628s2016 gw | s |||| 0|eng d
020			_a9783319321134 _9978-3-319-32113-4
050		4	_aRC321-580
072		7	_aPSAN _2bicssc
072		7	_aMED057000 _2bisacsh
082	0	4	_a612.8 _223
100	1		_aHo, Seng-Beng. _eauthor.
245	1	0	_aPrinciples of Noology _h[recurso electrónico] : _bToward a Theory and Science of Intelligence / _cby Seng-Beng Ho.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2016.
300			_aXIX, 431 p. 241 illus., 220 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
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, ?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.
650		0	_aMedicine.
650		0	_aScience.
650		0	_aNeurosciences.
650		0	_aArtificial intelligence.
650		0	_aComputational intelligence.
650	1	4	_aBiomedicine.
650	2	4	_aNeurosciences.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aComputational Intelligence.
650	2	4	_aScience, general.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783319321110
830		0	_aSocio-Affective Computing, _x2509-5706 ; _v3
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://dx.doi.org/10.1007/978-3-319-32113-4
912			_aZDB-2-SBL
942			_cLIBRO_ELEC
999			_c227889 _d227889