In this paper we introduce a Multi-agent system that uses Reinforcement Learning (RL) techniques to learn local navigational behaviors to simulate virtual pedestrian groups. The aim of the paper is to study empirically the validity of RL to learn agent-based navigation controllers and their transfer capabilities when they are used in simulation environments with a higher number of agents than in the learned scenario. Two RL algorithms which use Vector Quantization (VQ) as the generalization method for the space state are presented. Both strategies are focused on obtaining a good vector quantizier that represents adequately the state space of the agents. We empirically state the convergence of both methods in our navigational Multi-agent learning domain. Besides, we use validation tools of pedestrian models to analyze the simulation results in the context of pedestrian dynamics. The simulations carried out, scaling up the number of agents in our environment (a closed room with a door through which the agents have to leave), have revealed that the basic characteristics of pedestrian movements have been learned.
Abstract. In this paper we present a technique to build 3D maps of the environment using a 2D laser scanner combined with a robot's action model. This paper demonstrates that it is possible to build 3D maps in a cheap way using an angled 2D laser. We introduce a scan matching method to minimize the odometer errors of the robotics platform and a calibration method to improve the accuracy of the system. Some experimental results and conclusions are presented.
Nowadays, spam deliveries represent a major problem to benefit from the wide range of Internet-based communication forms. Despite the existence of different well-known intelligent techniques for fighting spam, only some specific implementations of Naïve Bayes algorithm are finally used in real environments for performance reasons. As long as some of these algorithms suffer from a large number of false positive errors, in this work we propose a rough set postprocessing approach able to significantly improve their accuracy. In order to demonstrate the advantages of the proposed method, we carried out a straightforward study based on a publicly available standard corpus (SpamAssassin), which compares the performance of previously successful well-known antispam classifiers (i.e., Support Vector Machines, AdaBoost, Flexible Bayes, and Naïve Bayes) with and without the application of our developed technique. Results clearly evidence the suitability of our rough set postprocessing approach for increasing the accuracy of previous successful antispam classifiers when working in real scenarios.
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