In this paper, we are introducing a new method to improve search engine capabilities by user preference achieved with the help of community's proxy logs. The goal is focused to build a custom search engine that providing community-specific results. To achieve such search engine, we use proxy server logs from Network Operation Center of EEPIS-ITS and fetch the url and user field as a base for our work. Then, we use tf-idf algorithm to convert those textual data into a machine friendly numerical data. To find topics based on those url, we cluster it into 10 or more preferable clusters using k-means algorithm. Getting the result of that method, then we crawl the title and meta information from all of the clustered url to find the actual topic. Those result, finally would be our base to create the search engine. Lastly, we use vector space model to provide a search result from user's query.
A novel approach to cooperative path-planning is presented. A SAT solver is used not to solve the whole instance but for optimizing the makespan of a sub-optimal solution. This approach is trying to exploit the ability of state-of-the-art SAT solvers to give a solution to relatively small instance quickly. A sub-optimal solution to the instance is obtained by some existent method first. It is then submitted to the optimization process which decomposes it into small subsequences for which optimal solutions are found by a SAT solver. The new shorter solution is subsequently obtained as concatenation of optimal sub-solutions. The process is iterated until a fixed point is reached. This is the first method to produce near optimal solutions for densely populated environments; it can be also applied to domain-independent planning supposed that sub-optimal planner is available.