This paper proposes to use prime implicants and prime implicates normal forms to represent belief sets. This representation is used, on the one hand, to define syntactical versions of belief change operators that also satisfy the rationality postulates but present better complexity properties than those proposed in the literature and, on the other hand, to propose a new minimal distance that adopts as a minimal belief unit a "fact", defined as a prime implicate of the belief set, instead of the usually adopted Hamming distance, i.e., the number of propositional symbols on which the models differ. Some experiments are also presented that show that this new minimal distance allows to define belief change operators that usually preserve more information of the original belief set.
Menu planning is a process appearing to be straightforward but many complexities arise when it is tried to be solved by computer means. Actually, although there is evidence of previous work since 50 years ago, at present there is no wide know tool which can solve this task in an automated manner. Also, not all proposals deal with full recipes along with considering the user food preferences. In this paper we propose a system architecture based on hybrid optimization: a first module based on mathematical programming, a well known robust approach to this problem; and a second module based on belief merging, a lesser known framework aimed to combine the nutrition scientist advices and policies along with the user food desires. The association of numerical and symbolic approaches will allow us to generate of a more agreeable menu. In order to illustrate our proposal, we present a motivating example detailing the main aspects of the system.
Guillain-Barré Syndrome (GBS) is a neurological disorder affecting people of any age and sex, mainly damaging the peripheral nervous system. GBS is divided into several subtypes, in which only four are the most common, demanding different treatments. Identifying the subtype is an expensive and time-consuming task. Early GBS detection is crucial to save the patient’s life and not aggravate the disease. This work aims to provide a primary screening tool for GBS subtypes fast and efficiently without complementary invasive methods, based only on clinical variables prospected in consultation, taken from clinical history, and based on risk factors. We conducted experiments with four classifiers with different approaches, five different filters for feature selection, six wrappers, and One versus All (OvA) classification. For the experiments, we used a data set that includes 129 records of Mexican patients and 26 clinical representative variables. Random Forest filter obtained the best results in each classifier for the diagnosis of the four subtypes, in the same way, this filter with the SVM classifier achieved the best result (0.6840). OvA with SVM classifier reached a balanced accuracy of 0.8884 for the Miller-Fisher (MF) subtype.
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