000			03800nam a22006255i 4500
001			978-3-540-73246-4
003			DE-He213
005			20160615111952.0
007			cr nn 008mamaa
008			100301s2009 gw | s |||| 0|eng d
020			_a9783540732464 _9978-3-540-73246-4
024	7		_a10.1007/978-3-540-73246-4 _2doi
049			_aAlfaisal Main Library
050		4	_aQ334-342
050		4	_aTJ210.2-211.495
072		7	_aUYQ _2bicssc
072		7	_aTJFM1 _2bicssc
072		7	_aCOM004000 _2bisacsh
082	0	4	_a006.3 _223
100	1		_ad’Avila Garcez, Artur S. _eauthor.
245	1	0	_aNeural-Symbolic Cognitive Reasoning _h[electronic resource] / _cby Artur S. d’Avila Garcez, Luís C. Lamb, Dov M. Gabbay.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2009.
300			_aXIV, 198 p. 53 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aCognitive Technologies, _x1611-2482
505	0		_aLogic and Knowledge Representation -- Artificial Neural Networks -- Neural-Symbolic Learning Systems -- Connectionist Modal Logic -- Connectionist Temporal Reasoning -- Connectionist Intuitionistic Reasoning -- Applications of Connectionist Nonclassical Reasoning -- Fibring Neural Networks -- Relational Learning in Neural Networks -- Argumentation Frameworks as Neural Networks -- Reasoning about Probabilities in Neural Networks -- Conclusions.
520			_aHumans are often extraordinary at performing practical reasoning. There are cases where the human computer, slow as it is, is faster than any artificial intelligence system. Are we faster because of the way we perceive knowledge as opposed to the way we represent it? The authors address this question by presenting neural network models that integrate the two most fundamental phenomena of cognition: our ability to learn from experience, and our ability to reason from what has been learned. This book is the first to offer a self-contained presentation of neural network models for a number of computer science logics, including modal, temporal, and epistemic logics. By using a graphical presentation, it explains neural networks through a sound neural-symbolic integration methodology, and it focuses on the benefits of integrating effective robust learning with expressive reasoning capabilities. The book will be invaluable reading for academic researchers, graduate students, and senior undergraduates in computer science, artificial intelligence, machine learning, cognitive science and engineering. It will also be of interest to computational logicians, and professional specialists on applications of cognitive, hybrid and artificial intelligence systems.
650		0	_aComputer science.
650		0	_aLogic.
650		0	_aComputers.
650		0	_aMathematical logic.
650		0	_aArtificial intelligence.
650		0	_aPattern recognition.
650	1	4	_aComputer Science.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aComputation by Abstract Devices.
650	2	4	_aTheory of Computation.
650	2	4	_aLogic.
650	2	4	_aMathematical Logic and Formal Languages.
650	2	4	_aPattern Recognition.
655		7	_aElectronic books. _2local
700	1		_aLamb, Luís C. _eauthor.
700	1		_aGabbay, Dov M. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783540732457
830		0	_aCognitive Technologies, _x1611-2482
856	4	0	_uhttp://ezproxy.alfaisal.edu/login?url=http://dx.doi.org/10.1007/978-3-540-73246-4
912			_aZDB-2-SCS
942			_2lcc _cEBOOKS
999			_c297138 _d297138