Talks@IDSIA: Two talks by Prof Markus Hutter
Dalle Molle Institute for Artificial Intelligence
Start date: 23 June 2009
End date: 27 June 2009
Markus Hutter is a former senior researcher of IDSIA who has recently moved to Australia, where is now Associate Professor in the RSISE at
the Australian National University in Canberra, Australia, and NICTA adjunct. He holds a PhD and BSc in physics and a Habilitation, MSc,
and BSc in informatics. Since 2000, his research is centered around the information-theoretic foundations of inductive reasoning and
reinforcement learning, which resulted in 50+ published research papers and several awards. His book "Universal Artificial Intelligence" (Springer, EATCS, 2005) develops the first sound and complete theory of AI. He also runs the Human Knowledge Compression Contest (50'000 Euro H-prize).
We are glad to host two special talks Professor Hutter will deliver on the 23rd and the 26th of June. The details follow at the end of this
message.
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When: 23 June 2009, 11h30
Where: Room 200, 2nd Floor, Galleria 2, Manno
What: Generic Reinforcement Learning Agents
Abstract:
In this fairly non-technical talk I will give an introduction to generic learning agents, and briefly discuss two recent instantiations.
Agent applications are ubiquitous in commerce and industry, and the sophistication, complexity, and importance of these applications is increasing rapidly; they include speech recognition systems, vision systems, search engines, auto-pilots, spam filters, and robots.
Current agent technology has the problems that the agents constructed are usually specialised to a narrow domain and require considerable
input from agent designers during construction. We can improve existing agent technology by making possible agent systems that can
automatically acquire (learn) during deployment much of the knowledge that is currently required to be built in by agent designers. This
will greatly reduce the effort required for agent construction and result in agents that are more
adaptive than at present and operate successfully in a wide variety of environments.
Recommended reading:
Universal Algorithmic Intelligence: A mathematical top-down approach http://www.hutter1.net/ai/aixigentle.pdf
Feature Markov Decision Processes
http://www.hutter1.net/ai/phimdp.pdf
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When: 26 June 2009, 11h30
Where: Room 200, 2nd Floor, Galleria 2, Manno
What: Feature Reinforcement Learning
Abstract:
In this more technical talk I will present a promising novel generic learning agent. It represents a general approach to learning that
bridges the gap between theory and practice in reinforcement learning (RL). General-purpose, intelligent, learning agents cycle through
sequences of observations, actions, and rewards that are complex, uncertain, unknown, and non-Markovian. On the other hand, RL is well-
developed for small finite-state Markov decision processes (MDPs). Up to now, extracting the right state representations out of bare observations, that is, reducing the general agent setup to the MDP
framework, is an art that involves significant effort by designers.
The primary goal of feature reinforcement learning is to automate the eduction process and thereby significantly expand the scope of many existing RL algorithms and the agents that employ them.
Papers:
Feature Reinforcement Learning: Part I. Unstructured MDPs
http://www.hutter1.net/ai/phimdpx.pdf
Feature Dynamic Bayesian Networks
http://www.hutter1.net/ai/phidbn.pdf
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Bio/CV:
http://www.hutter1.net/official/vitae.htm
