Search

To make artificial intelligence “see” the world is a primary step to making computers “learn”. Currently, videos are the most common visual resources for robots or artificial intelligence. This research focuses on interpreting the meaningless pixels into semantically meaningful objects. This thesis addresses making computers learn objects from videos automatically based on their motions. Results have shown that this approach can determine the moving objects in short video sequences (2 seconds), no matter how long it has been moving. The learned objects are effective for further recognizing the same objects in different videos. Further research is needed in order to be able to apply the same processes on longer video sequences and scenes that are more complicated.

In this paper, we present a learning technique for determining energy-reducing schedules for general devices and equipment. The proposed learning algorithm is based on Fitted-Q Iteration (FQI) and analyzes the usage and the users of a particular device to decide upon the appropriate profile of start-up and shut- down times. We experimentally evaluated our algorithm on a mixture of real-life and simulated data to discover that close-to-optimal control policies can be learned on a short timespan of a only few iterations. Our results show that the algorithm is capable of proposing intelligent schedules that minimize energy consumption and at the same time maximize user satisfaction.

In the last few decades there have been substantial improvements in approaches for solving the Boolean satisfiability problem. Many of these consisted in elaborating on existing algorithms, both on the side of complete solvers as in the area of incomplete solvers. Besides the improvements to existing solving methods, however, recent evolutions in SAT solving take the form of combining several solvers into one, resulting in parallel solvers and so-called hybrid solvers. Our goal is to combine both approaches, by presenting a parallel hybrid solver. The parallelism exists on two levels: we run a complete solver on the CPU concurrently with an incomplete solver on the GPU, where the latter in turn consists of a massively parallel local search algorithm. We implemented our approach using the OpenCL framework, and present preliminary experimental results. Beckers S., De Samblanx G., De Smedt F., Goedemé T., Struyf L., Vennekens J., ''Parallel hybrid SAT solving using OpenCL'', 24th Benelux conference on artificial intelligence - BNAIC-2012, pp. 11-18, October 25-26, 2012, Maastricht, The Netherlands. ispartof: pages:11-18 ispartof: Proceedings BNAIC 2012 pages:11-18 ispartof: Benelux Conference on Artificial Intelligence - BNAIC 2012 location:Maastricht, The Netherlands date:25 Oct - 26 Oct 2012 status: published