By Mario Alviano, Carmine Dodaro, Francesco Ricca (auth.), Matteo Baldoni, Cristina Baroglio, Guido Boella, Roberto Micalizio (eds.)
This e-book constitutes the refereed lawsuits of the thirteenth overseas convention of the Italian organization for synthetic Intelligence, AI*IA 2013, held in Turin, Italy, in December 2013. The forty five revised complete papers have been conscientiously reviewed and chosen from 86 submissions. The convention covers greatly the numerous features of theoretical and utilized synthetic Intelligence as follows: wisdom illustration and reasoning, desktop studying, traditional language processing, making plans, dispensed AI: robotics and MAS, recommender structures and semantic internet and AI applications.
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Extra info for AI*IA 2013: Advances in Artificial Intelligence: XIIIth International Conference of the Italian Association for Artificial Intelligence, Turin, Italy, December 4-6, 2013. Proceedings
Also for this reason we experience them as ‘necessities’, ‘forcing’ us to do or not to do something. Moreover, we conceptualize and conceive a “need” (either felt or not) as the lack of something, which is conceived not just as useful but as necessary; it is a necessary means, as the only possible means for achieving our goal: not only if I have O (what I need for G)6 I can realize G, but if I do not have O I cannot and will not realize G. This gives a sense of necessity, of no choice, to the general notion of “need”, which – in felt needs – is reinforced by the unpleasant sensation.
Concepts are represented by ovals, concept inclusions (is-a relationships) are denoted by dashed arrows, roles are denoted by solid arrows, and attributes are denoted by dots attached to classes hasGenerator, stating that every house has — possibly several — event generators attached to it. EventGenerator is the comprehensive class of elements that can generate diagnostic-relevant information. , the conceptualization of a temperature-reading device, and Apparatus can only be of type Warmer. The main concepts in the dynamic part of the ontology are DDSS which receives instances of IncomingEvent and sends instances of OutgoingEvent.
By using the mapping techniques described in . The algorithm used by ONDA to encode an OWL 2 QL ontology into the structure of a relational database is shown in Figure 2. addEdge(T , father) end if end if end for end procedure Fig. 2. Main algorithms of the Data Store Generator component – Read the ontology model from owlFile into the internal representation onto. – Parse onto and extract the map dataMap between concepts and datatype properties — function GET DATAT YPE P ROPERTIES — and the map relMap between concepts and object properties (roles) — function GET O BJECT P ROPERTIES.