L. Enrique Sucar

Enrique Sucar has a Ph.D in computing from Imperial College, London; a M.Sc. in electrical engineering from Stanford University; and a B.Sc. in electronics and communications engineering from ITESM, Monterrey, Mexico. He has been a Researcher at the Electrical Research Institute and Professor at ITESM Cuernavaca, and is currently a Senior Researcher at INAOE, Puebla, Mexico. He has more than 100 publications in journals and conference proceedings, and has directed 16 Ph.D. thesis. Dr. Sucar is Member of the National Research System, the Mexican Science Academy, and Senior Member of the IEEE. He has served as president of the Mexican AI Society, has been member of the Advisory Board of IJCAI, and is Associate Editor of the journals Computación y Sistemas and Revista Iberoamericana de Inteligencia Artificial. His main research interest are in graphical models and probabilistic reasoning, and their applications in computer vision, robotics and biomedicine.

Publications

Task Coordination for Service Robots Based on Multiple Markov Decision Processes
Task Coordination for Service Robots Based on Multiple Markov Decision Processes
Elva Corona, L. Enrique Sucar. © 2014. 18 pages.
Markov Decision Processes (MDPs) provide a principled framework for planing under uncertainty. However, in general they assume a single action per decision epoch. In service...
Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions
Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions
L. Enrique Sucar, Eduardo F. Morales, Jesse Hoey. © 2012. 444 pages.
One of the goals of artificial intelligence (AI) is creating autonomous agents that must make decisions based on uncertain and incomplete information. The goal is to design...
Task Coordination for Service Robots Based on Multiple Markov Decision Processes
Task Coordination for Service Robots Based on Multiple Markov Decision Processes
Elva Corona, L. Enrique Sucar. © 2012. 18 pages.
Markov Decision Processes (MDPs) provide a principled framework for planing under uncertainty. However, in general they assume a single action per decision epoch. In service...