SELF LEARNING METHODS TO FIND NOVEL INFORMATION
ON THE WWW
Inst of Information & Computing Sciences
This talk will discuss self-learning search methods for finding
novel interesting information on the world wide web (WWW).
The methods employ reinforcement learning (RL) agents which
can optimize their behavior by interacting with an environment
and learn from the obtained feedback (reward signals). The
reward for finding a novel WWW-page is given by a supervisedly
trained naive Bayesian classifier. The goal of the agent is
to maximize the cumulative reward obtained in its limited
life time, and thus it will learn to find as much novel interesting
WWW-pages as possible. The agents learn value functions for
evaluating many possible WWW-pages as starting address for
its search. It this way it can also learn to use particular
WWW-pages as starting
place which lead to finding many novel interesting pages.
As soon as the agent has exhaustively explored a WWW-page
and its links, it does not get any more reward, and it will
learn to search for different WWW-pages. The idea has been
proposed by Andrew McCallum, and we provide extensions to
Dr. Marco Wiering (email@example.com) finished his Computer Science
degree "Cum Laude" from the university of Amsterdam
in 1995. Then he did his PhD research at IDSIA (Lugano, CH).
He graduated on the PhD-thesis "Explorations in Efficient
Reinforcement Learning" in 1999. Since then we was a
postdoc at the UvA from May until October, 1999. Since January
2000, he works as a university researcher/teacher at the University
Utrecht. He published about 20 papers on the topics of reinforcement
learning, machine learning, and multi-agent systems.