By Janneke H. Bolt (auth.), Roberto Serra, Rita Cucchiara (eds.)
This e-book constitutes the refereed complaints of the eleventh overseas convention of the Italian organization for synthetic Intelligence, AI*IA 2009, held in Reggio Emilia, Italy, in December 2009.
The 50 revised complete papers awarded including three invited talks have been conscientiously reviewed and chosen from eighty three submissions. The papers are equipped in topical sections on wisdom illustration and reasoning, desktop studying, evolutionary computation, seek, common language processing, multi-agent structures and application.
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Additional resources for AI*IA 2009: Emergent Perspectives in Artificial Intelligence: XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia, Italy, December 9-12, 2009 Proceedings
80. IEEE Computer Society, Los Alamitos (2006) 5. : On the inﬂuence of description logics ontologies on conceptual similarity. , Euzenat, J. ) EKAW 2008. LNCS (LNAI), vol. 5268, pp. 48–63. Springer, Heidelberg (2008) 6. : Combining Answer Set Programming with Description Logics for the Semantic Web. In: Proc. KR 2004, pp. 141–151. AAAI Press, Menlo Park (2004) 7. : Rete: A Fast Algorithm for the Many Patterns/Many Objects Match Problem. Artif. Intell. 19(1), 17–37 (1982) 8. : Combining Horn Rules and Description Logics in Carin.
To this purpose we introduce a further score of rules: Deﬁnition 7. Given a rule r whose antecedents involves the classes C1 . . Cn , the speciﬁcity of r is deﬁned as follows: n specScore(r) = d(Ci ) (3) i=1 where: – d(Ci ) measures the level of Ci in the taxonomy and is deﬁned as: • d(Ci ) = 0 if Ci is a leaf • d(Ci ) = max(d(C1 ), . . d(Ck )) + 1 where C1 . . Ck are subclasses of Ci The ﬁnal score of a rule r can be obtained as a combination of score(r) and specScore(r). In the prototype we decided to base conﬂict resolution on score(r), selecting the rule with maximum score, using specScore(r) when the score is Common-Sense Rule Inference 27 the same and preferring the rule with minimum specScore.
The path length averaged over all pairs ˆ p in , whereas we consider of nodes. We have already discussed the importance of tw μ and π – originally defined in  to analyze the topology of large networks arising in practice – because they have been used in  to establish a relationship between the empirical hardness of SAT encodings and their structural properties. 0 for QBFs in the set k-open. In Figure 2 we show the results of our analysis on structural parameters. Looking at the figure, we can see that the set k-closed is characterized from a median value of μ ˆ p which is smaller than the same value for k-open formulas, whereas the value and tw 38 L.