Download ECAI 2006, 17th European Conference on Artificial by G. Brewka, S. Coradeschi, A. Perini and P. Traverso, Editors PDF

By G. Brewka, S. Coradeschi, A. Perini and P. Traverso, Editors

ISBN-10: 1586036424

ISBN-13: 9781586036423

In the summertime of 1956, John McCarthy prepared the recognized Dartmouth convention that is now ordinarily considered because the founding occasion for the sector of synthetic Intelligence. over the last 50 years, AI has visible an incredible improvement and is now a well-established medical self-discipline around the world. additionally in Europe AI is in first-class form, as witnessed by way of the massive variety of top of the range papers during this booklet. compared to ECAI 2004, there s a powerful bring up within the relative variety of submissions from allotted AI / brokers and Cognitive Modelling. wisdom illustration & Reasoning is ordinarily robust in Europe and continues to be the largest sector of ECAI 2006. One cause the figures for Case-Based Reasoning are particularly low is that a lot of the prime quality paintings during this quarter has came upon its means into prestigious functions and is hence represented below the heading of PAIS.

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822–830. Morgan Kaufmann, (1995). 16 ECAI 2006 G. Brewka et al. ) IOS Press, 2006 © 2006 The authors. All rights reserved. Comparing sets of positive and negative arguments: Empirical assessment of seven qualitative rules Jean-François Bonnefon 1 and Hélène Fargier2 Abstract. Many decisions can be represented as bipolar, qualitative sets of arguments: Arguments can be pros or cons, and ranked according to their importance, but not numerically evaluated. The problem is then to compare these qualitative, bipolar sets.

Background knowledge is represented by nonmonotonic consequence relations. , structural equations. , preference for abnormal factors, transitivity, coherence with logical entailment, and stability with respect to disjunction and conjunction. Empirical data are reported to support the psychological plausibility of our basic definitions. 1 INTRODUCTION Models of causal ascriptions crucially depend on the choice of an underlying representation for the causality-ascribing agent’s knowledge. Unlike standard diagnosis problems (wherein an unobserved cause must be inferred from observed events and known causal links), causality ascription is a problem of describing as ‘causal’ the link between two observed events in a sequence.

If the presence of mud on your plates does not allow to infer that you went to the countryside (perhaps you also regularly drive through muddy streets where you live), then transitivity is not applicable; you will only consider that the mud caused the fine, not that the trip did. 4 Entailment and causality ascriptions Classical entailment |= does not preserve ⇒ca . If C : A ⇒ca B and B |= B , one cannot say that C : A ⇒ca B . Indeed, while A ∧ C |∼ B follows by right weakening [18] from A ∧ C |∼ B, it is not generally true that C |∼ ¬B , given that C |∼ ¬B.

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