We formulate and analyze a new method for solving optimal control problems for systems governed by Volterra integral equations. Our method utilizes discretization of the original Volterra controlled system and a novel type of dynamic programming jn which the Hamilton-Jacobi function is parametrized by the control function (rather than the state, as in the case of ordinary dynamic programming). ...

Multi Objective Economic dispatch (MOED) problem has gained recent attention due to the deregulation of power industry and environmental regulations. So generating utilities should optimize their emission in addition to the operating cost. In this paper a Pareto frontier Differential Evolution (PDE) technique is developed to solve MOED problem, which provides a set of feasible solutions to the ...

Partially observable stochastic games (POSGs) provide a rich mathematical framework for planning under uncertainty by a group of agents. However, this modeling advantage comes with a price, namely a high computational cost. Solving POSGs optimally quickly becomes intractable after a few decision cycles. Our main contribution is to provide bounded approximation techniques, which enable us to sca...

A novel way to learn and track simultaneously the appearance of a previously non-seen face without intrusive techniques can be found in this article. The presented approach has a causal behaviour: no future frames are needed to process the current ones. The model used in the tracking process is refined with each input frame thanks to a new algorithm for the simultaneous and incremental computat...

Pruning hypotheses during dynamic programming is commonly used to speed up inference in settings such as parsing. Unlike prior work, we train a pruning policy under an objective that measures end-to-end performance: we search for a fast and accurate policy. This poses a difficult machine learning problem, which we tackle with the LOLS algorithm. LOLS training must continually compute the effect...

Many real-world tasks require making decisions that involve multiple possibly conflicting objectives. To succeed in such tasks, intelligent systems need planning or learning algorithms that can e ciently find di↵erent ways of balancing the trade-o↵s that such objectives present. In this tutorial, we provide an introduction to decision-theoretic approaches to coping with multiple objectives. We ...

A new algorithm for the incremental learning and non-intrusive tracking of the appearance of a previously non-seen face is presented. The computation is done in a causal fashion: the information for a given frame to be processed is combined only with the one of previous frames. To achieve this aim, a novel way for simultaneous and incremental computation of the Singular Value Decomposition (SVD...

This paper presents a method forfinding and tracking road lanes. The method extracts and tracks lane boundaries for vision-guided vehicle navigation by combining the hough transform and the “active line model ( A M ) ” . The hough transform can extract vanishing points of the road, which can be used as a good estimation of the vehicle heading. For the curved road, however, the estimation may be...

In this paper a kernel for time-series data is presented. The main idea of the kernel is that it is designed to recognize as similar time series that may be slightly shifted with one another. Namely, it tries to focus on the shape of the time-series and ignores the fact that the series may not be perfectly aligned. The proposed kernel has been validated on several datasets based on the UCR time...

The Outlier detection is currently area of active research in data set mining community. In this article we propose hybrid approach to capture outliers in dynamic data stream. We apply k-mean algorithm which Partition the data set into number of chunks or clusters. Each chunk contains set of data. Once cluster are formed, centroid of each cluster are calculated. The points which are lying near ...