A principled framework for modular web rule bases and its semantics

We present a principled framework for modular web rule bases, called MWeb. According to this framework, each predicate defined in a rule base is characterized by its defining reasoning mode, scope, and exporting rule base list. Each predicate used in a rule base is characterized by its requesting reasoning mode and importing rule base list. For valid MWeb modular rule bases S, theMWebAS andMWebWFS semantics of each rule base s ∈ S w.r.t. S are defined, model-theoretically. These semantics extend the answer set semantics (AS) and the well-founded semantics with explicit negation (WFSX) on ELPs, respectively, keeping all of their semantical and computational characteristics. Our framework supports: (i) local semantics and different points of view, (ii) local closed-world and open-world assumptions, (iii) scoped negation-as-failure, and (iv) restricted propagation of local inconsistencies. Additionally, it guarantees monotonicity of reasoning, in the case that new rule bases are added to the modular rule base, while the importing rule base list of the predicates of the old rule bases remains the same

An encompassing framework for Paraconsistent Logic Programs

AbstractWe propose a framework which extends Antitonic Logic Programs [Damásio and Pereira, in: Proc. 6th Int. Conf. on Logic Programming and Nonmonotonic Reasoning, Springer, 2001, p. 748] to an arbitrary complete bilattice of truth-values, where belief and doubt are explicitly represented. Inspired by Ginsberg and Fitting's bilattice approaches, this framework allows a precise definition of important operators found in logic programming, such as explicit and default negation. In particular, it leads to a natural semantical integration of explicit and default negation through the Coherence Principle [Pereira and Alferes, in: European Conference on Artificial Intelligence, 1992, p. 102], according to which explicit negation entails default negation. We then define Coherent Answer Sets, and the Paraconsistent Well-founded Model semantics, generalizing many paraconsistent semantics for logic programs. In particular, Paraconsistent Well-Founded Semantics with eXplicit negation (WFSXp) [Alferes et al., J. Automated Reas. 14 (1) (1995) 93–147; Damásio, PhD thesis, 1996]. The framework is an extension of Antitonic Logic Programs for most cases, and is general enough to capture Probabilistic Deductive Databases, Possibilistic Logic Programming, Hybrid Probabilistic Logic Programs, and Fuzzy Logic Programming. Thus, we have a powerful mathematical formalism for dealing simultaneously with default, paraconsistency, and uncertainty reasoning. Results are provided about how our semantical framework deals with inconsistent information and with its propagation by the rules of the program

Extended RDF as a Semantic Foundation of Rule Markup Languages

Ontologies and automated reasoning are the building blocks of the Semantic Web initiative. Derivation rules can be included in an ontology to define derived concepts, based on base concepts. For example, rules allow to define the extension of a class or property, based on a complex relation between the extensions of the same or other classes and properties. On the other hand, the inclusion of negative information both in the form of negation-as-failure and explicit negative information is also needed to enable various forms of reasoning. In this paper, we extend RDF graphs with weak and strong negation, as well as derivation rules. The ERDF stable model semantics of the extended framework (Extended RDF) is defined, extending RDF(S) semantics. A distinctive feature of our theory, which is based on Partial Logic, is that both truth and falsity extensions of properties and classes are considered, allowing for truth value gaps. Our framework supports both closed-world and open-world reasoning through the explicit representation of the particular closed-world assumptions and the ERDF ontological categories of total properties and total classes

Psychiatric Diagnosis from the Viewpoint of Computational Logic

While medical information systems have become common in the United States present systems have mostly addressed clerical aspects of medicine such as billing, record managementand similar tasks. Deeper problems, such as aiding the process of diagnosis, have largely remmained unexplored for commercial systems. This is not surprising since automating diagnosisrequires considerable sophistication both in the understanding of psychiatric epidemeology andin knowledge representation techniques. This paper is an interdisciplinary study of how recent results in logic programming, non-monotonic reasoning, and knowledge representation can aidin psychiatric diagnosis. We argue that to logically represent psychiatric diagnosis as codified in the Diagnostic and Statistical Manual of Mental Disorders, 4th edition requires abduction over programs that include both explicit and non-stratified default negation, as well as dynamic preference rules. We show how such programs can be translated into abductive frameworks over normal logic programs and implemented using recently introduced logic programming techhniques. Finally, we describe how such programs are used in a commercial product Diagnostica.authorsversionpublishe

Assessment of interventions in fuel management zones using remote sensing

Every year, wildfires strike the Portuguese territory and are a concern for public entities and the population. To prevent a wildfire progression and minimize its impact, Fuel Management Zones (FMZs) have been stipulated, by law, around buildings, settlements, along national roads, and other infrastructures. FMZs require monitoring of the vegetation condition to promptly proceed with the maintenance and cleaning of these zones. To improve FMZ monitoring, this paper proposes the use of satellite images, such as the Sentinel-1 and Sentinel-2, along with vegetation indices and extracted temporal characteristics (max, min, mean and standard deviation) associated with the vegetation within and outside the FMZs and to determine if they were treated. These characteristics feed machine-learning algorithms, such as XGBoost, Support Vector Machines, K-nearest neighbors and Random Forest. The results show that it is possible to detect an intervention in an FMZ with high accuracy, namely with an F1-score ranging from 90% up to 94% and a Kappa ranging from 0.80 up to 0.89.This work is supported by NOVA LINCS (UIDB/04516/2020) and ALGORITMI (UIDB/00319/2020) with the financial support of FCT- Fundação para a Ciencia e a Tecnologia, through national funds; This work is also supported by the project Floresta Limpa (PCIF/MOG/0161/2019

Deductive Diagnosis of Digital Circuits

In this paper we present an efficient deductive method for addressing combina- tional circuit diagnosis problems. The method resorts to bottom-up dependen- cies propagation, where truth-values are annotated with sets of faults. We com- pare it with several other logic programming techniques, starting with a naïve generate-and-test algorithm, and proceeding with a simple Prolog backtracking search. An approach using tabling is also studied, based on an abductive approach. For the sake of completeness, we also address the same problem with Answer Set Programming. Our tests recur to the ISCAS85 circuit bench- marks suite, although the technique is generalized to systems modelled by a set of propositional rules. The dependency-directed method outperforms others by orders of magnitude.authorsversionpublishe

Extended RDF: Computability and Complexity Issues

ERDF stable model semantics is a recently proposed semantics for ERDF ontologies and a faithful extension of RDFS semantics on RDF graphs. In this paper, we elaborate on the computability and complexity issues of the ERDF stable model semantics. Based on the undecidability result of ERDF stable model semantics, decidability under this semantics cannot be achieved, unless ERDF ontologies of restricted syntax are considered. Therefore, we propose a slightly modified semantics for ERDF ontologies, called ERDF #n- stable model semantics. We show that entailment under this semantics is, in general, decidable and also extends RDFS entailment. Equivalence statements between the two semantics are provided. Additionally, we provide algorithms that compute the ERDF #n-stable models of syntax-restricted and general ERDF ontologies. Further, we provide complexity results for the ERDF #nstable model semantics on syntax-restricted and general ERDF ontologies. Finally, we provide complexity results for the ERDF stable model semantics on syntax-restricted ERDF ontologies

Aumento do risco de ansiedade materna durante o surto de covid-19 no Brasil entre gestantes sem comorbidades

Objective: To studymaternal anxiety in pregnant womenwithout comorbidities in the context of the COVID-19 outbreak in Brazil and to study maternal knowledge and concerns about the pandemic. Methods: This is a secondary analysis from a national multicenter cross-sectional study performed in 10 cities, from June to August, 2020, in Brazil. Interviewed postpartum women, without medical or obstetrical comorbidities, were included in the present subanalysis. A structured questionnaire and the Beck Anxiety Inventory (BAI) were applied. Results: Out of the 1,662 women, 763 (45.9%)met the criteria for the current analysis and 16.1% presentedwithmoderate and 11.5% with severe maternal anxiety. Moderate or severe maternal anxiety was associated with high school education (odds ratio [OR]:1.58; 95% confidence interval [CI]:1.04–2.40). The protective factor was cohabiting with a partner (OR: 0.46; 95%CI: 0.29–0.73). There was a positive correlation between the total BAI score and receiving information about care in the pandemic (rpartial 0.15; p<0.001); concern about vertical transmission of COVID-19 (rpartial 0.10; p=0.01); receiving information about breastfeeding (rpartial 0.08; p¼0.03); concerns about prenatal care (rpartial 0.10; p¼0.01), and concerns about the baby contracting COVID-19 (rpartial 0.11; p=0.004). The correlation was negative in the following aspects: self-confidence in protecting from COVID-19 (rpartial 0.08; p¼0.04), having learned (rpartial 0.09; p=0.01) and self-confidence in breastfeeding (rpartial 0.22; p<0.001) in the context of the pandemic. Conclusion: The anxiety of pregnant women without medical or obstetrical comorbidities was associated to high school educational level and not living with a partner during the COVID-19 pandemic. Self-confidence in protecting against COVID-19 and knowledge about breastfeeding care during the pandemic reduced maternal anxiety.Objetivo: Estudar a ansiedade materna em gestantes sem comorbidades no contexto do surto de COVID-19 no Brasil e estudar o conhecimento e as preocupações maternas sobre a pandemia. Métodos: Trata-se de análise secundária de um estudo transversal multicêntrico nacional realizado em 10 cidades, de junho a agosto de 2020, no Brasil. Mulheres no pós-parto entrevistadas, semcomorbidadesmédicas ou obstétricas, foramincluídas nesta subanálise. Foram aplicados um questionário estruturado e o Inventário de Ansiedade de Beck (BAI, na sigla em inglês). Resultados: Das 1.662 mulheres, 763 (45,9%) atenderam aos critérios da análise atual e 16,1% apresentaram ansiedade materna moderada e 11,5% ansiedade materna grave. A ansiedade materna moderada ou grave foi associada à escolaridade no ensino médio (odds ratio [OR]: 1,58; intervalo de confiança [IC] 95%: 1,04–2,40). O fator protetor foi coabitar com companheiro (OR: 0,46; IC95%: 0,29–0,73). Houve correlação positiva entre a pontuação total do BAI e o recebimento de informações sobre cuidados na pandemia (rparcial 0,15; p<0,001); preocupação com a transmissão vertical de COVID-19 (rparcial 0,10; p=0,01); receber informações sobre amamentação (rparcial 0,08; p=0,03); preocupações sobre cuidados pré-natais (rparcial 0,10; p=0,01) e preocupações sobre o bebê contrair COVID-19 (rparcial 0,11; p=0,004). A correlação foi negativa com os seguintes aspectos: ter autoconfiança para se proteger (rparcial 0,08; p=0,04), aprender (rparcial 0,09; p=0,01) e ter autoconfiança para amamentar (rparcial 0,22; p<0,001) no contexto da pandemia. Conclusão: A ansiedade de gestantes sem comorbidades médicas ou obstétricas esteve associada à escolaridade no ensino médio e não morar com companheiro durante a pandemia de COVID-19. A autoconfiança na proteção contra COVID-19 e o conhecimento sobre os cuidados com a amamenta

Hybrid Probabilistic Logic Programs as Residuated Logic Programs

In this paper we show the embedding of Hybrid Probabilistic Logic Programs into the rather general framework of Residuated Logic Programs, where the main results of (definite) logic programming are validly extrapolated, namely the extension of the immediate consequences operator of van Emden and Kowalski. The importance of this result is that for the first time a framework encompassing several quite distinct logic programming semantics is described, namely Generalized Annotated Logic Programs, Fuzzy Logic Programming, Hybrid Probabilistic Logic Programs, and Possibilistic Logic Programming. Moreover, the embedding provides a more general semantical structure paving the way for defining paraconsistent probabilistic reasoning with a logic programming semantics