COLLOQUIUM
Department of Computer Science and Engineering
University of South Carolina
Goal-Oriented Self-Organizing Distributed Systems: A Study with Internet Crawlers
András Lorincz
Faculty of Informatics
Eötvös University
Date: August 23, 2006
Time: 1500-1600
Place: Swearingen 1A03 (Faculty Lounge)
Abstract
Social agents live and work in changing environments and they need to adapt to novel situations. Most of the environments feature small world characteristics. We are interested in the efficiency of different learning methods in scale-free worlds and in scale-free small worlds. We investigate how the topological structure of the environment influences algorithmic efficiency. We study the performances of algorithms, using selective learning, reinforcement learning, and their combinations, in random, scale-free, and scale-free small world (SFSW) environments. The learning problem was selected carefully: our agents search for novel, not-yet-found information. We ran our experiments on a large news site and on its downloaded portion. Controlled experiments were performed on this downloaded portion: we modified the topology, but preserved the publication time of the news. Our empirical results show that the selective learning is the most efficient in SFSW topology. In non-small world topologies, however, the combination of the selective and reinforcement learning algorithms performs the best. The influence of communication was studied, too. Results show that reinforcement learning may become important in certain topologies. We shall discuss the relevance of the different components of the experiments from the point fo view of 'intelligent' datamining and 'inventions'.
András Lorincz has been a professor and senior researcher at the Faculty of Informatics at Eötvös University, Budapest since 1998. His research focuses on distributed intelligent systems and their applications in neurobiological and cognitive modeling, as well as medicine. He founded the Neural Information Processing Group of Eötvös University and directs a multidisciplinary team of mathematicians, programmers, computer scientists and physicists. He acted as the PI of several successful international projects in collaboration with Panasonic, Honda Future Technology Research and the Information Directorate of the US Air Force in the fields of hardware-software co-synthesis, image processing and human-computer collaboration. He graduated in physics and received his PhD degree at the Eötvös Loránd University. He conducted research and taught at the Hungarian Academy of Sciences, University of Chicago, Brown University, Princeton University, the Illinois Institute of Technology and ETH Zurich. He authored about 200 peer reviewed scientific publications. He has received the Széchenyi Professor Award, Master Professor Award and the Széchenyi István Award in 2000, 2001, and 2004, respectively. Four of his students won the prestigious Pro Scientia Gold Medal in the field of information science over the last 4 years. In 2004, he was awarded the Kalmár Prize of the John von Neumann Computer Society of Hungary.