69 resultados para game-centred approaches

em Indian Institute of Science - Bangalore - Índia


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Recent research in modelling uncertainty in water resource systems has highlighted the use of fuzzy logic-based approaches. A number of research contributions exist in the literature that deal with uncertainty in water resource systems including fuzziness, subjectivity, imprecision and lack of adequate data. This chapter presents a broad overview of the fuzzy logic-based approaches adopted in addressing uncertainty in water resource systems modelling. Applications of fuzzy rule-based systems and fuzzy optimisation are then discussed. Perspectives on the scope for further research are presented.

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The minimum cost classifier when general cost functionsare associated with the tasks of feature measurement and classification is formulated as a decision graph which does not reject class labels at intermediate stages. Noting its complexities, a heuristic procedure to simplify this scheme to a binary decision tree is presented. The optimizationof the binary tree in this context is carried out using ynamicprogramming. This technique is applied to the voiced-unvoiced-silence classification in speech processing.

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The problem of learning correct decision rules to minimize the probability of misclassification is a long-standing problem of supervised learning in pattern recognition. The problem of learning such optimal discriminant functions is considered for the class of problems where the statistical properties of the pattern classes are completely unknown. The problem is posed as a game with common payoff played by a team of mutually cooperating learning automata. This essentially results in a probabilistic search through the space of classifiers. The approach is inherently capable of learning discriminant functions that are nonlinear in their parameters also. A learning algorithm is presented for the team and convergence is established. It is proved that the team can obtain the optimal classifier to an arbitrary approximation. Simulation results with a few examples are presented where the team learns the optimal classifier.

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A cooperative game played in a sequential manner by a pair of learning automata is investigated in this paper. The automata operate in an unknown random environment which gives a common pay-off to the automata. Necessary and sufficient conditions on the functions in the reinforcement scheme are given for absolute monotonicity which enables the expected pay-off to be monotonically increasing in any arbitrary environment. As each participating automaton operates with no information regarding the other partner, the results of the paper are relevant to decentralized control.

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Better operational control of water networks can help reduce leakage, maintain pressure, and control flow. Proportional integral derivative (PID) controllers, with proper fine-tuning, can help water utility operators achieve targets faster without creating undue transients. The authors compared three tuning methods, in different test situations, involving flow and level control to different reservoirs. Although target values were reached with all three tuning methods, the methods’ performances varied significantly. The lowest performer among the three was the method most widely used in the industry—standard tuning by the Ziegler-Nichols method. Achieving better results was offline tuning by genetic algorithms. Achieving the best control, though, was a fuzzy logic–based online tuning approach—the FZPID controller. The FZPID controller had fewer overshoots and took significantly less time to tune the gains for each problem. This new tuning approach for PID controllers can be applied to a variety of problems and can increase the performance of water networks of any size and structure

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The nonlinear singular integral equation of transonic flow is examined, noting that standard numerical techniques are not applicable in solving it. The difficulties in approximating the integral term in this expression were solved by special methods mitigating the inaccuracies caused by standard approximations. It was shown how the infinite domain of integration can be reduced to a finite one; numerical results were plotted demonstrating that the methods proposed here improve accuracy and computational economy.

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The overall architectural pattern of the mature plant is established during embryogenesis. Very little is known about the molecular processes that underlie embryo morphogenesis. Last decade has, nevertheless, seen a burst of information on the subject. The synchronous somatic embryogenesis system of carrot is largely being used as the experimental system. Information on the molecular regulation of embryogenesis obtained with carrot somatic embryos as well as observations on sandalwood embryogenic system developed in our laboratory are summarized in this review. The basic experimental strategy of molecular analysis mostly relied on a comparison between genes and proteins being expressed in embryogenic and non-embryogenic cells as well as in the different stages of embryogenesis. Events such as expression of totipotency of cells and establishment of polarity which are so critical for embryo development have been characterized using the strategy, Several genes have been identified and cloned from the carrot system, These include sequences that encode certain extracellular proteins (EPs) that influence cell proliferation and embryogenesis in specific ways and sequences of the abscisic acid (ABA) inducible late embryogenesis abundant (LEA) proteins which are most abundant and differentially expressed mRNAs in somatic embryos. That LEAs are expressed in the somatic embryos of a tree flora also is evidenced from studies on sandalwood Several undescribed or novel sequences that are enhanced in embryos were identified. A sequence of this nature exists in sandalwood embryos was demonstrated using a Cuscuta haustorial (organ-specific) cDNA probe. Somatic embryogenesis systems have been used to assess the expression of genes isolated from non-embryogenic tissues. Particular attention has been focused on both cell cycle and histone genes.

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It is demonstrated that the titled reactions are best carried out at high concentrations, as indicated by mechanistic considerations: the observed high reaction orders and the possibility that the Cannizzaro reaction is driven by the hydrophobic effect, which effects proximity between the two molecules of the aldehyde reactant. The present studies have led to improved conditions, simplified workup, and excellent yields of products. The Tishchenko reaction converted benzaldehyde to benzyl benzoate with catalytic NaOMe/tetrahydrafuran in good yield, which is apparently unprecedented for this product of high commercial value.

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Bacterial persistent infections are responsible for a significant amount of the human morbidity and mortality. Unlike acute bacterial infections, it is very difficult to treat persistent bacterial infections (e.g. tuberculosis). Knowledge about the location of pathogenic bacteria during persistent infection will help to treat such conditions by designing novel drugs which can reach such locations. In this study, events of bacterial persistent infections were analyzed using game theory. A game was defined where the pathogen and the host are the two players with a conflict of interest. Criteria for the establishment of Nash equilibrium were calculated for this game. This theoretical model, which is very simple and heuristic, predicts that during persistent infections pathogenic bacteria stay in both intracellular and extracellular compartments of the host. The result of this study implies that a bacterium should be able to survive in both intracellular and extracellular compartments of the host in order to cause persistent infections. This explains why persistent infections are more often caused by intracellular pathogens like Mycobacterium and Salmonella. Moreover, this prediction is in consistence with the results of previous experimental studies.

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In this paper we consider the task of prototype selection whose primary goal is to reduce the storage and computational requirements of the Nearest Neighbor classifier while achieving better classification accuracies. We propose a solution to the prototype selection problem using techniques from cooperative game theory and show its efficacy experimentally.

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In a three player quantum `Dilemma' game each player takes independent decisions to maximize his/her individual gain. The optimal strategy in the quantum version of this game has a higher payoff compared to its classical counterpart. However, this advantage is lost if the initial qubits provided to the players are from a noisy source. We have experimentally implemented the three player quantum version of the `Dilemma' game as described by Johnson, [N.F. Johnson, Phys. Rev. A 63 (2001) 020302(R)] using nuclear magnetic resonance quantum information processor and have experimentally verified that the payoff of the quantum game for various levels of corruption matches the theoretical payoff. (c) 2007 Elsevier Inc. All rights reserved.

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The problem of reconstruction of a refractive-index distribution (RID) in optical refraction tomography (ORT) with optical path-length difference (OPD) data is solved using two adaptive-estimation-based extended-Kalman-filter (EKF) approaches. First, a basic single-resolution EKF (SR-EKF) is applied to a state variable model describing the tomographic process, to estimate the RID of an optically transparent refracting object from noisy OPD data. The initialization of the biases and covariances corresponding to the state and measurement noise is discussed. The state and measurement noise biases and covariances are adaptively estimated. An EKF is then applied to the wavelet-transformed state variable model to yield a wavelet-based multiresolution EKF (MR-EKF) solution approach. To numerically validate the adaptive EKF approaches, we evaluate them with benchmark studies of standard stationary cases, where comparative results with commonly used efficient deterministic approaches can be obtained. Detailed reconstruction studies for the SR-EKF and two versions of the MR-EKF (with Haar and Daubechies-4 wavelets) compare well with those obtained from a typically used variant of the (deterministic) algebraic reconstruction technique, the average correction per projection method, thus establishing the capability of the EKF for ORT. To the best of our knowledge, the present work contains unique reconstruction studies encompassing the use of EKF for ORT in single-resolution and multiresolution formulations, and also in the use of adaptive estimation of the EKF's noise covariances. (C) 2010 Optical Society of America

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Synthetic approach to 3-alkoxythapsane, comprising of the carbon framework of a small group of sesquiterpenes containing three contiguous quaternary carbon atoms has been described. A combination of alkylation, orthoester Claisen rearrangement and intramolecular diazoketone cyclopropanation has been employed for the creation of the three requisite contiguous quaternary carbon atoms.

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Automatic identification of software faults has enormous practical significance. This requires characterizing program execution behavior and the use of appropriate data mining techniques on the chosen representation. In this paper, we use the sequence of system calls to characterize program execution. The data mining tasks addressed are learning to map system call streams to fault labels and automatic identification of fault causes. Spectrum kernels and SVM are used for the former while latent semantic analysis is used for the latter The techniques are demonstrated for the intrusion dataset containing system call traces. The results show that kernel techniques are as accurate as the best available results but are faster by orders of magnitude. We also show that latent semantic indexing is capable of revealing fault-specific features.

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In this article, the problem of two Unmanned Aerial Vehicles (UAVs) cooperatively searching an unknown region is addressed. The search region is discretized into hexagonal cells and each cell is assumed to possess an uncertainty value. The UAVs have to cooperatively search these cells taking limited endurance, sensor and communication range constraints into account. Due to limited endurance, the UAVs need to return to the base station for refuelling and also need to select a base station when multiple base stations are present. This article proposes a route planning algorithm that takes endurance time constraints into account and uses game theoretical strategies to reduce the uncertainty. The route planning algorithm selects only those cells that ensure the agent will return to any one of the available bases. A set of paths are formed using these cells which the game theoretical strategies use to select a path that yields maximum uncertainty reduction. We explore non-cooperative Nash, cooperative and security strategies from game theory to enhance the search effectiveness. Monte-Carlo simulations are carried out which show the superiority of the game theoretical strategies over greedy strategy for different look ahead step length paths. Within the game theoretical strategies, non-cooperative Nash and cooperative strategy perform similarly in an ideal case, but Nash strategy performs better than the cooperative strategy when the perceived information is different. We also propose a heuristic based on partitioning of the search space into sectors to reduce computational overhead without performance degradation.