Presentaciones a Congresos
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Browsing Presentaciones a Congresos by Subject "AUTOMATIZACION"
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ponencia en congreso.listelement.badge Bounded exhaustive search of alloy specification repairs(2021) Gutiérrez Brida, Simón; Regis, Germán; Zheng, Guolong; Bagher, Hamid; Nguyen, Thanh Vu; Aguirre, Nazareno; Frías, Marcelo"The rising popularity of declarative languages and the hard to debug nature thereof have motivated the need for applicable, automated repair techniques for such languages. However, despite significant advances in the program repair of imperative languages, there is a dearth of repair techniques for declarative languages. This paper presents BeAFix, an automated repair technique for faulty models written in Alloy, a declarative language based on first-order relational logic. BeAFix is backed with a novel strategy for bounded exhaustive, yet scalable, ex ploration of the spaces of fix candidates and a formally rigorous, sound pruning of such spaces. Moreover, different from the state of-the-art in Alloy automated repair, that relies on the availability of unit tests, BeAFix does not require tests and can work with assertions that are naturally used in formal declarative languages. Our experience with using BeAFix to repair thousands of real world faulty models, collected by other researchers, corroborates its ability to effectively generate correct repairs and outperform the state-of-the-art."ponencia en congreso.listelement.badge Learning by knowledge sharing in autonomous intelligent systems(2006) García Martínez, Ramón; Borrajo, Daniel; Maceri, Pablo; Britos, Paola Verónica"Very few learning systems applied to problem solving have focused on learning operator definitions from the interaction with a completely unknown environment. In order to achieve better learning convergence, several agents that learn separately are allowed to interchange each learned set of planning operators. Learning is achieved by establishing plans, executing those plans in the environment, analyzing the results of the execution, and combining new evidence with prior evidence. Operators are generated incrementally by combining rote learning, induction, and a variant of reinforcement learning. The results show how allowing the communication among individual learning (and planning) agents provides a much better percentage of successful plans, plus an improved convergence rate than the individual agents alone."