Case-based reasoning for meta-heuristics self-parameterization in a multi-agent scheduling system

A novel agent-based approach to Meta-Heuristics self-configuration is proposed in this work. Meta-heuristics are examples of algorithms where parameters need to be set up as efficient as possible in order to unsure its performance. This paper presents a learning module for self-parameterization of Meta-heuristics (MHs) in a Multi-Agent System (MAS) for resolution of scheduling problems. The learning is based on Case-based Reasoning (CBR) and two different integration approaches are proposed. A computational study is made for comparing the two CBR integration perspectives. In the end, some conclusions are reached and future work outlined.

Case-based reasoning for self-optimizing behavior

In this paper we present a Self-Optimizing module, inspired on Autonomic Computing, acquiring a scheduling system with the ability to automatically select a Meta-heuristic to use in the optimization process, so as its parameterization. Case-based Reasoning was used so the system may be able of learning from the acquired experience, in the resolution of similar problems. From the obtained results we conclude about the benefit of its use.

Constraint reasoning for differential models

The basic motivation of this work was the integration of biophysical models within the interval constraints framework for decision support. Comparing the major features of biophysical models with the expressive power of the existing interval constraints framework, it was clear that the most important inadequacy was related with the representation of differential equations. System dynamics is often modelled through differential equations but there was no way of expressing a differential equation as a constraint and integrate it within the constraints framework. Consequently, the goal of this work is focussed on the integration of ordinary differential equations within the interval constraints framework, which for this purpose is extended with the new formalism of Constraint Satisfaction Differential Problems. Such framework allows the specification of ordinary differential equations, together with related information, by means of constraints, and provides efficient propagation techniques for pruning the domains of their variables. This enabled the integration of all such information in a single constraint whose variables may subsequently be used in other constraints of the model. The specific method used for pruning its variable domains can then be combined with the pruning methods associated with the other constraints in an overall propagation algorithm for reducing the bounds of all model variables. The application of the constraint propagation algorithm for pruning the variable domains, that is, the enforcement of local-consistency, turned out to be insufficient to support decision in practical problems that include differential equations. The domain pruning achieved is not, in general, sufficient to allow safe decisions and the main reason derives from the non-linearity of the differential equations. Consequently, a complementary goal of this work proposes a new strong consistency criterion, Global Hull-consistency, particularly suited to decision support with differential models, by presenting an adequate trade-of between domain pruning and computational effort. Several alternative algorithms are proposed for enforcing Global Hull-consistency and, due to their complexity, an effort was made to provide implementations able to supply any-time pruning results. Since the consistency criterion is dependent on the existence of canonical solutions, it is proposed a local search approach that can be integrated with constraint propagation in continuous domains and, in particular, with the enforcing algorithms for anticipating the finding of canonical solutions. The last goal of this work is the validation of the approach as an important contribution for the integration of biophysical models within decision support. Consequently, a prototype application that integrated all the proposed extensions to the interval constraints framework is developed and used for solving problems in different biophysical domains.

Gender and racial inequalities in trends of oral cancer mortality in Sao Paulo, Brazil

OBJECTIVE:To analyse recent trends in oral cancer mortality, focusing specifically on differences concerning gender and race.METHODS:Official information on deaths and population in the city of Sao Paulo, 2003 to 2009, were used to estimate mortality rates from oral cancer (C00 to C10, International Classification of Diseases, 10th Revision), adjusted for age and stratified by gender (females and males) and race (blacks and whites). The Prais-Winsten auto-regression procedure was used to analyse the time series.RESULTS:During the study period, 8,505 individuals living in the city of Sao Paulo died of oral cancer. Rates increased for females (rate of yearly increase = 4.4%, 95%CI 1.4;7.5), and levelled off for men, which represents an inversion of previous trends among genders in the city. Increases were identified for blacks, with a high rate of yearly increase of 9.1% (95%CI 5.5;12.9), and levelled off for whites. Oral cancer mortality in blacks almost doubled during the study period, and surpassed mortality in whites for almost all categories.CONCLUSIONS:Mortality presented a higher increase among women than in men, and it doubled among backs. The surveillance of trends of oral cancer mortality across gender and racial groups may contribute to implementing socially appropriate health policies, which concurrently reduce the burden of disease and the attenuation of unfair, avoidable and unnecessary inequalities in health.

Meta-heuristics self-parameterization in a multi-agent scheduling system using case-based reasoning

This paper proposes a novel agent-based approach to Meta-Heuristics self-configuration. Meta-heuristics are algorithms with parameters which need to be set up as efficient as possible in order to unsure its performance. A learning module for self-parameterization of Meta-heuristics (MH) in a Multi-Agent System (MAS) for resolution of scheduling problems is proposed in this work. The learning module is based on Case-based Reasoning (CBR) and two different integration approaches are proposed. A computational study is made for comparing the two CBR integration perspectives. Finally, some conclusions are reached and future work outlined.

A Logic and tool for local reasoning about security protocols

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

A comparative study between centralization and decentralization of diagnostic reasoning in EPS

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa de Lisboa para obtenção do grau de mestre em Engenharia Electrotécnica e de Computadores

Conservation GIS: Ontology and spatial reasoning for commonsense knowledge.

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Combining open and closed world reasoning for the semantic web

Dissertação para obtenção do Grau de Doutor em Informática

A qualitive reasoning approach for improving query results for sketch based queries by topological analysis of spatial aggregation

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Probabilistic constraint reasoning

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Informática, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Composição racial e a avaliação da reação intradérmica de Montenegro em portadores da leishmaniose cutâneo-mucosa

Em um estudo retrospectivo, indivíduos com a forma mucosa da leishmaniose foram pareados (sexo, idade e ocupação) a outros pacientes com a forma cutânea em atividade, não demonstrando-se diferença significante da freqüência nos grupos raciais. Do mesmo modo, o teste intradérmico de Montenegro não se mostrou diferente nos diversos grupos raciais, entre os 96 pares de pacientes estudados. Uma maior enduração foi observada em pacientes com o acometimento mucoso quando comparado aos cutâneos.

Forma hepatosplênica da esquistossomose mansônica, em relação à composição racial e ao nível sócio-econômico, em Catolândia-Bahia

De 1125 indivíduos, residentes em área hiperendêmica de esquistossomose mansônica, estudou-se a apresentação clínica, relacionada aos grupos raciais (mestiço de índio, branco, mulato-claro, mulato-médio, mulato-escuro e negro) e ao nível sócio- econômico, conforme o somatório de 16 variáveis. Em 229 indivíduos brancos havia 24 (10,5%) com a forma hepatosplênica, significantemente superior, em comparação, com 32 (3,6%) dos 896 não-brancos; entre os grupos raciais negroides ocorreram freqüências semelhantes. Os hepatosplênicos apresentaram nível sócío-econômíco mais alto e quando, também, brancos, o risco relativo (2,78) foi marcadamente superior.

Constraint reasoning with local search for continuous optimization

Optimization is a very important field for getting the best possible value for the optimization function. Continuous optimization is optimization over real intervals. There are many global and local search techniques. Global search techniques try to get the global optima of the optimization problem. However, local search techniques are used more since they try to find a local minimal solution within an area of the search space. In Continuous Constraint Satisfaction Problems (CCSP)s, constraints are viewed as relations between variables, and the computations are supported by interval analysis. The continuous constraint programming framework provides branch-and-prune algorithms for covering sets of solutions for the constraints with sets of interval boxes which are the Cartesian product of intervals. These algorithms begin with an initial crude cover of the feasible space (the Cartesian product of the initial variable domains) which is recursively refined by interleaving pruning and branching steps until a stopping criterion is satisfied. In this work, we try to find a convenient way to use the advantages in CCSP branchand- prune with local search of global optimization applied locally over each pruned branch of the CCSP. We apply local search techniques of continuous optimization over the pruned boxes outputted by the CCSP techniques. We mainly use steepest descent technique with different characteristics such as penalty calculation and step length. We implement two main different local search algorithms. We use “Procure”, which is a constraint reasoning and global optimization framework, to implement our techniques, then we produce and introduce our results over a set of benchmarks.

Probabilistic constraint reasoning with Monte Carlo integration to robot localization

This work studies the combination of safe and probabilistic reasoning through the hybridization of Monte Carlo integration techniques with continuous constraint programming. In continuous constraint programming there are variables ranging over continuous domains (represented as intervals) together with constraints over them (relations between variables) and the goal is to find values for those variables that satisfy all the constraints (consistent scenarios). Constraint programming “branch-and-prune” algorithms produce safe enclosures of all consistent scenarios. Special proposed algorithms for probabilistic constraint reasoning compute the probability of sets of consistent scenarios which imply the calculation of an integral over these sets (quadrature). In this work we propose to extend the “branch-and-prune” algorithms with Monte Carlo integration techniques to compute such probabilities. This approach can be useful in robotics for localization problems. Traditional approaches are based on probabilistic techniques that search the most likely scenario, which may not satisfy the model constraints. We show how to apply our approach in order to cope with this problem and provide functionality in real time.