28,678 research outputs found
Human Dorsal Striatal Activity during Choice Discriminates Reinforcement Learning Behavior from the Gamblerâs Fallacy
Reinforcement learning theory has generated substantial interest in neurobiology, particularly because of the resemblance between phasic dopamine and reward prediction errors. Actorâcritic theories have been adapted to account for the functions of the striatum, with parts of the dorsal striatum equated to the actor. Here, we specifically test whether the human dorsal striatumâas predicted by an actorâcritic instantiationâis used on a trial-to-trial basis at the time of choice to choose in accordance with reinforcement learning theory, as opposed to a competing strategy: the gambler's fallacy. Using a partial-brain functional magnetic resonance imaging scanning protocol focused on the striatum and other ventral brain areas, we found that the dorsal striatum is more active when choosing consistent with reinforcement learning compared with the competing strategy. Moreover, an overlapping area of dorsal striatum along with the ventral striatum was found to be correlated with reward prediction errors at the time of outcome, as predicted by the actorâcritic framework. These findings suggest that the same region of dorsal striatum involved in learning stimulusâresponse associations may contribute to the control of behavior during choice, thereby using those learned associations. Intriguingly, neither reinforcement learning nor the gambler's fallacy conformed to the optimal choice strategy on the specific decision-making task we used. Thus, the dorsal striatum may contribute to the control of behavior according to reinforcement learning even when the prescriptions of such an algorithm are suboptimal in terms of maximizing future rewards
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A unified model of the electrical power network
Traditionally, the different infrastructure layers, technologies and management activities associated with the design, control and protection operation of the Electrical Power Systems have been supported by numerous independent models of the real world network. As a result of increasing competition in this sector, however, the integration of technologies in the network and the coordination of complex management processes have become of vital importance for all electrical power companies.
The aim of the research outlined in this paper is to develop a single network model which will unify the generation, transmission and distribution infrastructure layers and the various alternative implementation technologies. This 'unified model' approach can support ,for example, network fault, reliability and performance analysis. This paper introduces the basic network structures, describes an object-oriented modelling approach and outlines possible applications of the unified model
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Update of an early warning fault detection method using artificial intelligence techniques
This presentation describes a research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial Neural Networks (ANNs) are being used as the core of the fault detector. In an earlier paper [11], a computer simulated medium length transmission line has been tested by the detector and the results clearly demonstrate the capability of the detector. Todayâs presentation considers a case study illustrating the suitability of this AI Technique when applied to a distribution transformer. Furthermore, an evolutionary optimisation strategy to train ANNs is also briefly discussed in this presentation, together with a âcrystal ballâ view of future developments in the operation and monitoring of transmission systems in the next millennium
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Power system fault prediction using artificial neural networks
The medium term goal of the research reported in this paper was the development of a major in-house suite of strategic computer aided network simulation and decision support tools to improve the management of power systems. This paper describes a preliminary research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. To achieve this goal, an AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system . Simulation will normally take place using equivalent circuit representation. Artificial Neural Networks (ANNs) are used to construct a hierarchical feed-forward structure which is the most important component in the fault detector. Simulation of a transmission line (2-port circuit ) has already been carried out and preliminary results using this system are promising. This approach provided satisfactory results with accuracy of 95% or higher
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Early warning fault detection using artificial intelligent methods
This paper describes a research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial Neural Networks (ANNs) are being used as the core of the fault detector. A simulated medium length transmission line has been tested by the detector and the results demonstrate the capability of the detector. Furthermore, comments on an evolutionary technique as the optimisation strategy for ANNs are included in this paper
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Gaining assurance in a voter-verifiable voting system
The literature on e-voting systems has many examples of discussion of the correctness of the computer and communication algorithms of such systems, as well as discussions of their vulnerabilities. However, a gap in the literature concerns the practical need (before adoption of a specific e-voting system) for a complete case demonstrating that the system as a whole has sufficiently high probability of exhibiting the desired properties when in use in an actual election. This paper discusses the problem of producing such a case, with reference to a specific system: a version of the PrĂȘt Ă Voter scheme for voter-verifiable e-voting. We show a possible organisation of a case in terms of four main requirements â accuracy, privacy, termination and âtrustednessââ and show some of the detailed organisation that such a case should have, the diverse kinds of evidence that needs to be gathered and some of the interesting difficulties that arise
Intersection Information based on Common Randomness
The introduction of the partial information decomposition generated a flurry
of proposals for defining an intersection information that quantifies how much
of "the same information" two or more random variables specify about a target
random variable. As of yet, none is wholly satisfactory. A palatable measure of
intersection information would provide a principled way to quantify slippery
concepts, such as synergy. Here, we introduce an intersection information
measure based on the G\'acs-K\"orner common random variable that is the first
to satisfy the coveted target monotonicity property. Our measure is imperfect,
too, and we suggest directions for improvement.Comment: 19 pages, 5 figure
An increase in under hydrostatic pressure in the superconducting doped topological insulator NbBiSe
We report an unexpected positive hydrostatic pressure derivative of the
superconducting transition temperature in the doped topological insulator \NBS
via SQUID magnetometry in pressures up to 0.6 GPa. This result is contrary
to reports on the homologues \CBS and \SBS where smooth suppression of is
observed. Our results are consistent with recent Ginzburg-Landau theory
predictions of a pressure-induced enhancement of in the nematic
multicomponent state proposed to explain observations of rotational
symmetry breaking in doped BiSe superconductors.Comment: 5 pages, 5 figure
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