This paper presents a Bayesian model to predict the opening (first strategy) of opponents in real-time strategy (RTS) games. Our model is general enough to be applied to any RTS game with the canonical gameplay of gat...
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ISBN:
(纸本)9781457700095
This paper presents a Bayesian model to predict the opening (first strategy) of opponents in real-time strategy (RTS) games. Our model is general enough to be applied to any RTS game with the canonical gameplay of gathering resources to extend a technology tree and produce military units and we applied it to StarCraft1. This model can also predict the possible technology trees of the opponent, but we will focus on openings here. The parameters of this model are learned from replays (game logs), labeled with openings. We present a semi-supervised method of labeling replays with the expectation-maximization algorithm and key features, then we use these labels to learn our parameters and benchmark our method with cross-validation. Uses of such a model range from a commentary assistant (for competitive games) to a core component of a dynamic RTS bot/AI, as it will be part of our StarCraft AI competition entry bot.
We present two phase noise estimation algorithms for single-carrier burst communications. The first and second estimation techniques (denoted as PEA1 and PEA2 respectively) are based on a truncated discrete cosine tra...
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ISBN:
(纸本)9781424493326
We present two phase noise estimation algorithms for single-carrier burst communications. The first and second estimation techniques (denoted as PEA1 and PEA2 respectively) are based on a truncated discrete cosine transform (DCT) basis expansion of the phase noise and of the corresponding phasor, respectively and do not require any knowledge of the phase noise statistics. An initial pilot-based estimate is iteratively improved by means of the expectation-maximization algorithm, yielding a soft-decision-directed phase noise estimate. The performances of PEA1 and PEA2 are compared in terms of the mean-square phase error and bit-error rate. Numerical results show that PEA2 out performs PEA1 for practical values of the signal-to-noise ratio.
In this paper we address the joint estimation of phase noise (PHN) and channel impulse response (CIR) in orthogonal frequency division multiplexing (OFDM) systems. We solve the estimation problem utilizing an algorith...
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ISBN:
(纸本)9781424492688
In this paper we address the joint estimation of phase noise (PHN) and channel impulse response (CIR) in orthogonal frequency division multiplexing (OFDM) systems. We solve the estimation problem utilizing an algorithm based on a Monte Carlo (MC) implementation of the expectation-maximization (EM) algorithm. Our approach exploits the linear and Gaussian structure associated with the transmitted signal. We also focus on the impact that inaccurate estimation of PHN bandwidth has on the accuracy of the channel estimates. We show the benefits of the proposed algorithm via simulation studies.
The continual reassessment method (CRM) is a commonly used dose-finding design for phase I clinical trials. Practical applications of this method have been restricted by two limitations: (1) the requirement that the t...
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The continual reassessment method (CRM) is a commonly used dose-finding design for phase I clinical trials. Practical applications of this method have been restricted by two limitations: (1) the requirement that the toxicity outcome needs to be observed shortly after the initiation of the treatment;and (2) the potential sensitivity to the prespecified toxicity probability at each dose. To overcome these limitations, we naturally treat the unobserved toxicity outcomes as missing data, and use the expectation-maximization (EM) algorithm to estimate the dose toxicity probabilities based on the incomplete data to direct dose assignment. To enhance the robustness of the design, we propose prespecifying multiple sets of toxicity probabilities, each set corresponding to an individual CRM model. We carry out these multiple CRMs in parallel, across which model selection and model averaging procedures are used to make more robust inference. We evaluate the operating characteristics of the proposed robust EM-CRM designs through simulation studies and show that the proposed methods satisfactorily resolve both limitations of the CRM. Besides improving the MTD selection percentage, the new designs dramatically shorten the duration of the trial, and are robust to the prespecification of the toxicity probabilities.
We present a mixture cure model with the survival time of the "uncured" group coming from a class of linear transformation models, which is an extension of the proportional odds model This class of model. fi...
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We present a mixture cure model with the survival time of the "uncured" group coming from a class of linear transformation models, which is an extension of the proportional odds model This class of model. first proposed by Dabrowska and Doksum ( 988). which we term "generalized proportional odds model," is well suited for the mixture cure model setting due to a clear separation between long-term and short-term effects A standard expectation-maximization algorithm can he employed to locate the nonparametric likelihood estimators which are shown to he consistent and semiparametric efficient However. there are difficulties in the M-step due to the nonparametric component We overcome these difficulties by proposing two different algorithms The first is to employ an majorize-minimize (MM) algorithm in the M-step instead of the usual Newton-Raphson method, and the other is based on an alternative form to express the model as a proportional hazards frailty model The two new algorithms are compared in a simulation study with an existing estimating equation approach by DJ and Ying (2004) The MM algorithm provides both computational stability and efficiency A case study of leukemia dam is conducted to illustrate the proposed procedures
Spectrum sharing employs dynamic allocation of frequency resources for a more efficient use of the radio spectrum. Despite its appealing features, this technology inevitably complicates the synchronization task, which...
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Spectrum sharing employs dynamic allocation of frequency resources for a more efficient use of the radio spectrum. Despite its appealing features, this technology inevitably complicates the synchronization task, which must be accomplished in an environment that may involve interference. This paper considers the uplink of an orthogonal frequency-division multiple-access (OFDMA)-based spectrum sharing system and provides solutions for estimating the frequency and timing errors of multiple unsynchronized users. In doing so, we exploit suitably designed training blocks and apply maximum likelihood methods after modeling the interference power on each subcarrier as a random variable with an inverse gamma distribution. The resulting frequency estimator turns out to be the extension to a multiuser scenario of a scheme that has previously been proposed for single-user spectrum-sharing systems. Timing recovery is more challenging and leads to a complete search over a multidimensional domain. To overcome such a difficulty, two alternative approaches are proposed. The first one relies on a simplifying assumption about the interference distribution in the frequency domain, while the second scheme operates in an iterative fashion according to the expectation-maximization algorithm.
This paper illustrates the use of mixture of experts (ME) network structure to guide model selection for diagnosis of two subtypes of adult hydrocephalus (normal-pressure hydrocephalus-NPH and aqueductal stenosis-AS)....
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This paper illustrates the use of mixture of experts (ME) network structure to guide model selection for diagnosis of two subtypes of adult hydrocephalus (normal-pressure hydrocephalus-NPH and aqueductal stenosis-AS). The ME is a modular neural network architecture for supervised learning. expectation-maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. To improve classification accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. The classifiers were trained on the defining features of NPH and AS (velocity and flux). Three types of records (normal, NPH and AS) were classified with the accuracy of 95.83% by the ME network structure. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models.
Image feature extraction and similarity measure in feature space are active research topics. They are basic components in a content-based image retrieval (CBIR) system. In this letter, we present a new statistical mod...
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Image feature extraction and similarity measure in feature space are active research topics. They are basic components in a content-based image retrieval (CBIR) system. In this letter, we present a new statistical model-based image feature extraction method in the wavelet domain and a novel Kullback divergence-based similarity measure. First, a Gaussian mixture model (GMM) and a more systematic generalized Gaussian mixture model (GGMM) are employed to describe the statistical characteristics of the wavelet coefficients and the model parameters are employed to construct a compact image feature space. A nontrivial expectation-maximization (EM) algorithm for the GGMM model is derived. Subsequently, a new Kullback divergence-based similarity measure with low-computation cost is derived and analyzed. The Brodatz texture image database and some other image databases are used to evaluate the retrieval performance based on the presented new methods. Experimental results indicate that the GMM and the GGMM-based image texture features are very effective in representing multiscale image characteristics and that the new methods outperforms other conventional wavelet-based methods in retrieval performance with a comparable level of computational complexity. It is also demonstrated that for image features extracted by the new statistical models, the similarity measure based on Kullback divergence is more effective than conventional similarity measures.
Estimating the ratio of two probability density functions (a.k.a. the importance) has recently gathered a great deal of attention since importance estimators can be used for solving various machine learning and data m...
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Estimating the ratio of two probability density functions (a.k.a. the importance) has recently gathered a great deal of attention since importance estimators can be used for solving various machine learning and data mining problems. In this paper, we propose a new importance estimation method using a mixture of probabilistic principal component analyzers. The proposed method is more flexible than existing approaches, and is expected to work well when the target importance function is correlated and rank-deficient. Through experiments, we illustrate the validity of the proposed approach.
We consider the problem of joint non-data-aided (NDA) estimation of carrier frequency and phase offsets, together with that of signal and noise powers, obtained from the baud-rate samples of a linearly modulated signa...
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We consider the problem of joint non-data-aided (NDA) estimation of carrier frequency and phase offsets, together with that of signal and noise powers, obtained from the baud-rate samples of a linearly modulated signal distorted by Gaussian noise. The Cramer-Rao lower bound (CRLB) is derived, for quadrature-symmetric constellations showing that the carrier parameters are decoupled from signal and noise powers. Furthermore, a joint NDA maximum-likelihood estimator is developed, based on the application of the expectation-maximization algorithm, which exhibits a jitter performance close to the CRLB for a wide SNR range.
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