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DTSTART:20221030T030000
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UID:calendar.24943.field_data.0@oba.diag.uniroma1.it
DTSTAMP:20260407T203247Z
CREATED:20221111T113300Z
DESCRIPTION:Abstract: Reinforcement-learning agents optimize their behavior
  from evaluative feedback. This talk focuses on how we're improving reinfo
 rcement-learning algorithms by building a better understanding of how peop
 le can provide this feedback through telling\, training\, and teaching. Sp
 ecifically\, it will cover linear temporal logic as a task representation\
 , arguing that traditional rewards can make behaviors difficult to express
 . It will also summarize work that attempts to characterize the ways human
  users deliver evaluative feedback and novel algorithms that can use this 
 feedback effectively as a way of enabling end-user training of AI systems.
 Bio: Michael L. Littman is a University Professor of Computer Science at B
 rown University\, studying machine learning and decision making under unce
 rtainty. He has earned multiple university-level awards for teaching and h
 is research on reinforcement learning\, probabilistic planning\, and autom
 ated crossword-puzzle solving has been recognized with three best-paper aw
 ards and three influential paper awards. Littman is co-director of Brown's
  Humanity Centered Robotics Initiative and a Fellow of the Association for
  the Advancement of Artificial Intelligence and the Association for Comput
 ing Machinery. He is also a Fellow of the American Association for the Adv
 ancement of Science Leshner Leadership Institute for Public Engagement wit
 h Science\,focusing on Artificial Intelligence.
DTSTART;TZID=Europe/Paris:20221117T163000
DTEND;TZID=Europe/Paris:20221117T163000
LAST-MODIFIED:20221111T114233Z
LOCATION:Online (Zoom): https://uniroma1.zoom.us/j/99323809469?pwd=QklieXJt
 MkFzZkQvRUhvMWNON2tSUT09
SUMMARY:Programming Agents via Evaluative Feedback - Michael Littman - Mich
 ael Littman
URL;TYPE=URI:http://oba.diag.uniroma1.it/node/24943
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