Get AI*IA 2013: Advances in Artificial Intelligence: XIIIth PDF

By Mario Alviano, Carmine Dodaro, Francesco Ricca (auth.), Matteo Baldoni, Cristina Baroglio, Guido Boella, Roberto Micalizio (eds.)

ISBN-10: 3319035231

ISBN-13: 9783319035239

ISBN-10: 331903524X

ISBN-13: 9783319035246

This booklet 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 rigorously reviewed and chosen from 86 submissions. The convention covers widely the various points of theoretical and utilized synthetic Intelligence as follows: wisdom illustration and reasoning, computer studying, common language processing, making plans, allotted AI: robotics and MAS, recommender platforms and semantic net and AI applications.

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Read or Download AI*IA 2013: Advances in Artificial Intelligence: XIIIth International Conference of the Italian Association for Artificial Intelligence, Turin, Italy, December 4-6, 2013. Proceedings PDF

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Example text

The key element of the rules model is the Ideal House actor shown in Figure 4 (iii). This actor simulates the behavior of an environment in which a warmer alternates between two states: a heating state, wherein the warmer is switched on and it contributes to heat up the environment, and a cooling state, wherein the warmer is switched off and the environment cools down because of the natural heat dispersion. The dynamics of temperature in each state is simulated with a simple linear differential equation of the kind T˙ = −Kc T + Kw , where T is the temperature, T˙ is its time-derivative, Kc is the heatdispersion coefficient of the walls, and Kw is the warmer heating rate.

In this way, a very simple model-based diagnosis of the real 2 The actor Plot is used only for debugging purposes to track signals from Ideal House and the real household. Towards an Ontology-Based Framework 35 Fig. 5. Experimental results about the HVAC case study comparing the performances of the DDDS generated by ONDA (squares) and those of a manually coded DDSS (triangles). The plot on the left is about 10 monitored houses simulated for an increasing number of days. The plot on the right is about monitoring for 10 days an increasing number of houses.

The main concepts in the static part of the domain are House and EventGenerator. They are related by Towards an Ontology-Based Framework 33 Fig. 3. Domain ontology for HVAC monitoring. 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.

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AI*IA 2013: Advances in Artificial Intelligence: XIIIth International Conference of the Italian Association for Artificial Intelligence, Turin, Italy, December 4-6, 2013. Proceedings by Mario Alviano, Carmine Dodaro, Francesco Ricca (auth.), Matteo Baldoni, Cristina Baroglio, Guido Boella, Roberto Micalizio (eds.)


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