000			04568nam a22005535i 4500
001			978-3-540-32235-1
003			DE-He213
005			20240730194523.0
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008			100715s2005 gw | s |||| 0|eng d
020			_a9783540322351 _9978-3-540-32235-1
024	7		_a10.1007/b107184 _2doi
050		4	_aQ334-342
050		4	_aTA347.A78
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
072		7	_aUYQ _2thema
082	0	4	_a006.3 _223
245	1	0	_aConditionals, Information, and Inference _h[electronic resource] : _bInternational Workshop, WCII 2002, Hagen, Germany, May 13-15, 2002, Revised Selected Papers / _cedited by Gabriele Kern-Isberner, Wilhelm Rödder, Friedhelm Kulmann.
250			_a1st ed. 2005.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2005.
300			_aXII, 219 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v3301
505	0		_aInvited Papers -- What Is at Stake in the Controversy over Conditionals -- Reflections on Logic and Probability in the Context of Conditionals -- Acceptance, Conditionals, and Belief Revision -- Regular Papers -- Getting the Point of Conditionals: An Argumentative Approach to the Psychological Interpretation of Conditional Premises -- Projective Default Epistemology -- On the Logic of Iterated Non-prioritised Revision -- Assertions, Conditionals, and Defaults -- A Maple Package for Conditional Event Algebras -- Conditional Independences in Gaussian Vectors and Rings of Polynomials -- Looking at Probabilistic Conditionals from an Institutional Point of View -- There Is a Reason for Everything (Probably): On the Application of Maxent to Induction -- Completing Incomplete Bayesian Networks.
520			_aConditionals are fascinating and versatile objects of knowledge representation. On the one hand, they may express rules in a very general sense, representing, for example, plausible relationships, physical laws, and social norms. On the other hand, as default rules or general implications, they constitute a basic tool for reasoning, even in the presence of uncertainty. In this sense, conditionals are intimately connected both to information and inference. Due to their non-Boolean nature, however, conditionals are not easily dealt with. They are not simply true or false - rather, a conditional "if A then B" provides a context, A, for B to be plausible (or true) and must not be confused with "A entails B" or with the material implication "not A or B." This ill- trates how conditionals represent information, understood in its strict sense as reduction of uncertainty. To learn that, in the context A, the proposition B is plausible, may reduce uncertainty about B and hence is information. The ab- ity to predict such conditioned propositions is knowledge and as such (earlier) acquired information. The ?rst work on conditional objects dates back to Boole in the 19th c- tury, and the interest in conditionals was revived in the second half of the 20th century, when the emerging Arti?cial Intelligence made claims for appropriate formaltoolstohandle"generalizedrules."Sincethen,conditionalshavebeenthe topic of countless publications, each emphasizing their relevance for knowledge representation, plausible reasoning, nonmonotonic inference, and belief revision.
650		0	_aArtificial intelligence. _93407
650		0	_aMachine theory. _9157345
650	1	4	_aArtificial Intelligence. _93407
650	2	4	_aFormal Languages and Automata Theory. _9157346
700	1		_aKern-Isberner, Gabriele. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9157347
700	1		_aRödder, Wilhelm. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9157348
700	1		_aKulmann, Friedhelm. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9157349
710	2		_aSpringerLink (Online service) _9157350
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783540253327
776	0	8	_iPrinted edition: _z9783540809098
830		0	_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v3301 _9157351
856	4	0	_uhttps://doi.org/10.1007/b107184
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