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.