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The traditional method for extracting features from image is accomplished by Hough Transform. When an image is represented mathematically, it becomes easy to perform computation according to a certain algorithm and detect the required features from the image. The presence of such features is determined by a voting procedure in a parameter space, from which the selected features are obtained as local maxima by creating a certain threshold. This procedure for simple detection of lines was patented by Paul V.C. Hough in 1962. There has been various advancement in this field for detecting lines and circles, all of them are based on the parent Hough Transform way to vote for the corresponding features. The various ways of detecting features and the mathematics behind them is discussed in this report.

All of the basic search agents are discussed using the Berkeley AI materials of Pacman interface.

The work of finding the best places according to user’s preferences can be a tedious task. We designed, created and tested software which can take the preferences from the user and provide the best places according to their preferences from the location with a certain radius in real time. This eliminates the extra task of researching about a place and helps the user to visualise the places according to the preferences in real time. This uses foursquare API to fetch the data for the locations. We have built a console application for this purpose and this can be transferred to web based system in future. The internet connection is a must during using of this application. The Map that is formed is visualised in a browser and can be saved for future use. We have provided certain features in the application that will improve the user’s experience.

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