Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
Solving the science data downlink problem for Mars Express (MEX) has been a challenge. An artificial intelligence (AI) based tool, MEXAR2, has been developed and integrated in the mission planning process that allows the user to quickly and effortlessly ge ...
Users often have to search for a most preferred item but do not know how to state their preferences in the language allowed by the system. Example-Critiquing has been proposed as a mixed-initiative technique for allowing them to construct their preference ...
American Association for Artificial Intelligence, Menlo Park, CA 94025-3496, United States2005
An accurate model of the user's preferences is a crucial element of most decision support systems. It is often assumed that users have a well-defined and stable set of preferences that can be elicited through a set of questions. However, recent research ha ...
American Association for Artificial Intelligence, Menlo Park, CA 94025-3496, United States2005
We provide an overview of the evolutionary approach to the emergence of artificial intelligence in embodied behavioral agents. This approach, also known as Evolutionary Robotics, builds and capitalizes upon the interactions between the embodied agent and i ...
The SAB conference brings together researchers from ethology, psychology, ecology, artificial intelligence, artificial life, robotics, computer science, engineering, and related fields to further understanding of the behaviors and underlying mechanisms tha ...
By using other agents experiences and knowledge, a learning agent may learn faster, make fewer mistakes, and create some rules for unseen situations. These benefits would be gained if the learning agent can extract proper rules out of the other agents know ...
The objective of this document is to present a rapid dialogue prototyping methodology developed at the Artificial Intelligent Laboratory - Ecole Polytechnique Fédérale de Lausanne. Concretely, the rapid dialogue prototyping methodology is decomposed into 5 ...
Automatic face analysis has to cope with pose and lighting variations. Especially pose variations are difficult to tackle and many face analysis methods require the use of sophisticated normalization procedures. We propose a data-driven face analysis appro ...
A group of cooperative and homogeneous Q-learning agents can cooperate to learn faster and gain more knowledge. In order to do so, each learner agent must be able to evaluate the expertness and the intelligence level of the other agents and to assess the k ...
Automatic face analysis has to cope with pose and lighting variations. Especially pose variations are difficult to tackle and many face analysis methods require the use of sophisticated normalization procedures. We propose a data-driven face analysis appro ...