Search results for "probabilistic logic"

showing 10 items of 253 documents

Detecting faulty wireless sensor nodes through Stochastic classification

2011

In many distributed systems, the possibility to adapt the behavior of the involved resources in response to unforeseen failures is an important requirement in order to significantly reduce the costs of management. Autonomous detection of faulty entities, however, is often a challenging task, especially when no direct human intervention is possible, as is the case for many scenarios involving Wireless Sensor Networks (WSNs), which usually operate in inaccessible and hostile environments. This paper presents an unsupervised approach for identifying faulty sensor nodes within a WSN. The proposed algorithm uses a probabilistic approach based on Markov Random Fields, requiring exclusively an ana…

Brooks–Iyengar algorithmComputer scienceDistributed computingReal-time computingProbabilistic logicMarkov processMarkov Random Fieldsymbols.namesakeKey distribution in wireless sensor networksWireless Sensor Networks.Autonomic ComputingSensor nodesymbolsOverhead (computing)Algorithm designWireless sensor network2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)
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Construction sequence analysis of long-span cable-stayed bridges

2018

Abstract In cantilever construction of long-span cable-stayed bridges the stressing sequence of stays is fundamental for establishing the final configuration of the bridge. The structural behaviour of these bridges is usually evaluated through a forward staged construction analysis, in which the values of the prestressing forces to be applied to stays are the main unknowns. A unified procedure for determining the initial cable forces and for analyzing the entire sequence is presented here, considering the geometric nonlinearity of stays through the Dischinger equivalent elastic modulus. The target is the simultaneous determination of the initial cable forces with the simulation of the const…

CantileverComputer science0211 other engineering and technologiesNAPS020101 civil engineering02 engineering and technologyBridge (nautical)0201 civil engineeringDeckStress (mechanics)Effects of uncertainties021105 building & constructionPylonForward analysisCivil and Structural EngineeringSequenceConstruction sequencebusiness.industryProbabilistic logicProbabilistic approachCable-stayed structures; Construction sequence; Effects of uncertainties; Forward analysis; NAPS; Partial scheme; PES; Probabilistic approach; Civil and Structural EngineeringStructural engineeringPESSettore ICAR/09 - Tecnica Delle CostruzioniNonlinear systemPartial schemebusinessCable-stayed structures
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Combined effect of solvent content, temperature and pH on the chromatographic behaviour of ionisable compounds. III: Considerations about robustness

2009

Abstract We previously reported a model able to predict the retention time of ionisable compounds as a function of the solvent content, temperature and pH [J. Chromatogr. A 1163 (2007) 49]. The model was applied further, developing an optimisation of the resolution based on the peak purity concept [J. Chromatogr. A 1193 (2008) 117]. However, we left aside an important issue: we did not consider incidental overlaps caused by shifts in the predicted peak positions, owing either to uncertainties in the source data, modelling errors, or the practical implementation in the chromatograph of the optimal mobile phase (or any other). These shifts can ruin the predicted separation, since they can eas…

ChromatographyChromatographyLogarithmChemistryOrganic ChemistryMonte Carlo methodTemperatureProbabilistic logicReproducibility of ResultsGeneral MedicineFunction (mathematics)Reversed-phase chromatographyHydrogen-Ion ConcentrationBiochemistryAnalytical ChemistryDistribution (mathematics)Models ChemicalRobustness (computer science)Phase (matter)SolventsComputer SimulationOrganic ChemicalsProtonsJournal of Chromatography A
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Emergent Collective Behaviors in a Multi-agent Reinforcement Learning Pedestrian Simulation: A Case Study

2015

In this work, a Multi-agent Reinforcement Learning framework is used to generate simulations of virtual pedestrians groups. The aim is to study the influence of two different learning approaches in the quality of generated simulations. The case of study consists on the simulation of the crossing of two groups of embodied virtual agents inside a narrow corridor. This scenario is a classic experiment inside the pedestrian modeling area, because a collective behavior, specifically the lanes formation, emerges with real pedestrians. The paper studies the influence of different learning algorithms, function approximation approaches, and knowledge transfer mechanisms on performance of learned ped…

Collective behaviorFunction approximationbusiness.industryComputer scienceBellman equationVector quantizationProbabilistic logicReinforcement learningArtificial intelligencebusinessTransfer of learningKnowledge transferSimulation
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Improved Constructions of Quantum Automata

2008

We present a simple construction of quantum automata which achieve an exponential advantage over classical finite automata. Our automata use $\frac{4}{\epsilon} \log 2p + O(1)$ states to recognize a language that requires p states classically. The construction is both substantially simpler and achieves a better constant in the front of logp than the previously known construction of [2]. Similarly to [2], our construction is by a probabilistic argument. We consider the possibility to derandomize it and present some preliminary results in this direction.

CombinatoricsDiscrete mathematicsFinite-state machineSimple (abstract algebra)Quantum automataProbabilistic logicQuantum finite automataConstant (mathematics)MathematicsAutomatonExponential function
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Values of games with probabilistic graphs

1999

Abstract In this paper we consider games with probabilistic graphs. The model we develop is an extension of the model of games with communication restrictions by Myerson (1977) . In the Myerson model each pair of players is joined by a link in the graph if and only if these two players can communicate directly. The current paper considers a more general setting in which each pair of players has some probability of direct communication. The value is defined and characterized in this context. It is a natural extension of the Myerson value and it turns out to be the Shapley value of a modified game.

Computer Science::Computer Science and Game TheorySociology and Political ScienceIf and only ifComputingMilieux_PERSONALCOMPUTINGProbabilistic logicGeneral Social SciencesStatistics Probability and UncertaintyDirect communicationShapley valueMathematical economicsGeneral PsychologyGraphMathematics
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REPEATED GAMES WITH PROBABILISTIC HORIZON

2005

Repeated games with probabilistic horizon are defined as those games where players have a common probability structure over the length of the game's repetition, T. In particular, for each t, they assign a probability pt to the event that "the game ends in period t". In this framework we analyze Generalized Prisoners' Dilemma games in both finite stage and differentiable stage games. Our construction shows that it is possible to reach cooperative equilibria under some conditions on the distribution of the discrete random variable T even if the expected length of the game is finite. More precisely, we completely characterize the existence of sub-game perfect cooperative equilibria in finite s…

Computer Science::Computer Science and Game TheorySociology and Political ScienceSequential gameProbabilistic logicComputingMilieux_PERSONALCOMPUTINGGeneral Social SciencesPrisoner's dilemmaConvergence (routing)Repeated gameApplied mathematicsrepeated games probabilistic horizon cooperationDifferentiable functionStatistics Probability and UncertaintyMathematical economicsRandom variableGeneral PsychologyMathematicsEvent (probability theory)
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Memory limited inductive inference machines

1992

The traditional model of learning in the limit is restricted so as to allow the learning machines only a fixed, finite amount of memory to store input and other data. A class of recursive functions is presented that cannot be learned deterministically by any such machine, but can be learned by a memory limited probabilistic leaning machine with probability 1.

Computer Science::Machine LearningClass (set theory)Computer scienceInductive biasProbabilistic logicRecursive functionsLimit (mathematics)Inductive reasoningAlgorithm
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Seismic evaluation of ordinary RC buildings retrofitted with externally bonded FRPs using a reliability-based approach

2020

International audience; Despite the extensive literature on reinforced concrete (RC) members retrofitted with fiberreinforced polymer (FRP) composites, few studies have employed a reliability-based approach to evaluate the seismic performance of RC buildings in terms of their collapse capacity and ductility. In this study, the performance of a poorly-confined RC building structure is investigated for different FRP retrofitting schemes using different configurations and combinations of wrapping and flange-bonded FRPs, as two well-established techniques. A nonlinear pushover analysis is then implemented with a computational reliability analysis based on Latin Hypercube Sampling (LHS) to deter…

Computer science02 engineering and technologyRetrofitting0203 mechanical engineeringRC buildings[PHYS.MECA.SOLID]Physics [physics]/Mechanics [physics]/Solid mechanics [physics.class-ph]RetrofittingCollapse capacityDuctilityReliability (statistics)Civil and Structural EngineeringDuctilitybusiness.industryProbabilistic logicFailure modeStructural engineeringFibre-reinforced plastic021001 nanoscience & nanotechnologyReliability020303 mechanical engineering & transportsLatin hypercube samplingCeramics and Composites0210 nano-technologybusinessMaterial propertiesFailure mode and effects analysisFRP
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Iteratively Learning a Liver Segmentation Using Probabilistic Atlases: Preliminary Results

2016

This works deals with the concept of liver segmentation by using a priori information based on probabilistic atlases and segmentation learning based of previous steps. A probabilistic atlas is here understood as a probability or membership map that tells how likely is that a point belongs to a shape drawn from the shape distribution at hand. We devise a procedure to segment Perfusion Magnetic Resonance liver images that combines both: a probabilistic atlas of the liver and a segmentation algorithm based on global information of previous simpler segmentation steps, local information from close segmented slices and finally a mathematical morphology procedure, namely viscous reconstruction, to…

Computer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentation02 engineering and technologyIterative reconstructionMathematical morphology030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicine0202 electrical engineering electronic engineering information engineeringmedicineSegmentationComputer visionComputingMethodologies_COMPUTERGRAPHICSmedicine.diagnostic_testSegmentation-based object categorizationbusiness.industryProbabilistic logicMagnetic resonance imagingPattern recognitionImage segmentationComputer Science::Computer Vision and Pattern Recognition020201 artificial intelligence & image processingArtificial intelligencebusinessPerfusion2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)
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