Interpreting, modeling and representing emotions is a key feature of new generation games. This paper describes the first version of the Emotional Engine we have developed as a component of more complex behavior simulators. The purpose of this module is to manage the state and behavior of the characters present in a scene while they interact with a human user. We use preexistent language recognition libraries like Windows™ Speech API, and Kinect™ devices to communicate real humans with artificial characters participating in a virtual scene. The Emotional Engine works upon numeric variables extracted from such devices and calculated after some natural language interpretation process. It then produces numerical results that lead the behavior, modify both the verbal and body language of the characters, and influence the general evolution of the scene that takes place inside the simulator. This paper presents the system architecture and discusses some key components, such as the Language Interpretation and the Body Language Interpreter modules.
This paper deals with the automatic classification of customers on the basis of their movements around a sports shop center. We start by collecting coordinates from customers while they visit the store. Consequently, any costumer’s path through the shop is formed by a list of coordinates, obtained with a frequency of one measurement per minute. A guess about the trajectory is constructed, and a number of parameters are calculated before performing a Clustering Process. As a result, we can identify several types of customers, and the dynamics of their behavior inside the shop. We can also monitor the state of the shop, identify different situations that appear during limited periods of time, and predict peaks in customer traffic.
This paper describes our research on using Genetic Programming to obtain transition rules for Cellular Automata, which are one type of massively parallel computing system. Our purpose is to determine the existence of a limit of chaos for three dimensional Cellular Automata, empirically demonstrated for the two dimensional case. To do so, we must study statistical properties of 3D Cellular Automata over long simulation periods. When dealing with big three dimensional meshes, applying the transition rule to the whole structure can become a extremely slow task. In this work we decompose the Automata into pieces and use OpenMp to parallelize the process. Results show that using a decomposition procedure, and distributing the mesh between a set of processors, 3D Cellular Automata can be studied without having long execution times.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.