000			03352nam a22005175i 4500
001			978-94-91216-11-4
003			DE-He213
005			20140220083838.0
007			cr nn 008mamaa
008			111201s2011 fr | s |||| 0|eng d
020			_a9789491216114 _9978-94-91216-11-4
024	7		_a10.2991/978-94-91216-11-4 _2doi
050		4	_aQA75.5-76.95
072		7	_aUY _2bicssc
072		7	_aCOM014000 _2bisacsh
082	0	4	_a004 _223
100	1		_aGoertzel, Ben. _eauthor.
245	1	0	_aReal-World Reasoning: Toward Scalable, Uncertain Spatiotemporal, Contextual and Causal Inference _h[electronic resource] / _cby Ben Goertzel, Nil Geisweiller, Lucio Coelho, Predrag Janičić, Cassio Pennachin.
264		1	_aParis : _bAtlantis Press, _c2011.
300			_aIX, 269 p. 59 illus., 1 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		_aAtlantis Thinking Machines, _x1877-3273 ; _v1
505	0		_aIntroduction -- Knowledge Representation Using Formal Logic -- Quantifying and Managing Uncertainty -- Representing Temporal Knowledge -- Temporal Reasoning -- Representing and Reasoning On Spatial Knowledge -- Representing and Reasoning on Contextual Knowledge -- Causal Reasoning -- Extracting Logical Knowledge from Raw Data -- Scalable Spatiotemporal Logical Knowledge Storage -- Mining Patterns from Large Spatiotemporal Logical Knowledge Stores -- Probabilistic Logic Networks -- Temporal and Contextual Reasoning in PLN -- Inferring the Causes of Observed Changes.-Adaptive Inference Control.
520			_aThe general problem addressed in this book is a large and important one: how to usefully deal with huge storehouses of complex information about real-world situations. Every one of the major modes of interacting with such storehouses – querying, data mining, data analysis – is addressed by current technologies only in very limited and unsatisfactory ways. The impact of a solution to this problem would be huge and pervasive, as the domains of human pursuit to which such storehouses are acutely relevant is numerous and rapidly growing. Finally, we give a more detailed treatment of one potential solution with this class, based on our prior work with the Probabilistic Logic Networks (PLN) formalism. We show how PLN can be used to carry out realworld reasoning, by means of a number of practical examples of reasoning regarding human activities inreal-world situations.
650		0	_aComputer science.
650		0	_aArtificial intelligence.
650		0	_aInformation Systems.
650	1	4	_aComputer Science.
650	2	4	_aComputer Science, general.
650	2	4	_aManagement of Computing and Information Systems.
650	2	4	_aArtificial Intelligence (incl. Robotics).
700	1		_aGeisweiller, Nil. _eauthor.
700	1		_aCoelho, Lucio. _eauthor.
700	1		_aJaničić, Predrag. _eauthor.
700	1		_aPennachin, Cassio. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9789491216107
830		0	_aAtlantis Thinking Machines, _x1877-3273 ; _v1
856	4	0	_uhttp://dx.doi.org/10.2991/978-94-91216-11-4
912			_aZDB-2-SCS
999			_c109732 _d109732