Multiagent Planning in Partially Observable Uncertain Worlds

Speaker:        Professor Shlomo Zilberstein
                University of Massachusetts, Amherst

Title:          "Multiagent Planning in Partially Observable Uncertain
                 Worlds"

Date:           Friday, 2 August 2013

Time:           2:00pm - 3:00pm

Venue:          Room 3501 (via lifts 25/26), HKUST

Abstract:

Coordinating the operation of a group of decision makers or agents in
stochastic environments is a long-standing challenge in AI.  Decision
theory offers a normative framework for optimizing decisions under
uncertainty. But due to computational complexity barriers, developing
decision-theoretic planning algorithms for multiagent systems is a serious
challenge.  We describe a range of new formal models and algorithms to
tackle this problem.  Exact algorithms shed light on the structure and
complexity of the problem, but they have limited use as only tiny problems
can be solved optimally.  We describe a number of effective approximation
techniques that use bounded memory, sampling, and randomization.  These
methods can produce high-quality results in a variety of application
domains such as mobile robot coordination and sensor network management.
We examine the performance of these algorithms and describe current
research efforts to further improve their applicability and scalability.


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Biogrpahy:

Shlomo Zilberstein is professor of computer science at the University of
Massachusetts, Amherst.  He received his Ph.D. from UC Berkeley and his
B.A. from the Technion.  His research focuses on the foundations of
automated planning and the development of formal models of rational
behavior in situations characterized by uncertainty and limited
computational resources.  He is a fellow of the Association for the
Advancement of Artificial Intelligence, the former editor-in-chief of the
Journal of Artificial Intelligence Research, former president of ICAPS and
a current councillor of AAAI..