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X-WR-CALNAME:Decomposition Bounds for Influence Diagrams
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UID:2021-02-08-junkyu-lee@cml.ics.uci.edu
DTSTAMP:20210208T000000Z
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SUMMARY:[CML Seminar] Junkyu Lee: Decomposition Bounds for Influence Diagra
ms
LOCATION:Online (live stream)
DESCRIPTION:Junkyu Lee\, AI Planning Group\, IBM Research\n\nTitle: Decompo
sition Bounds for Influence Diagrams\n\nAbstract: Influence diagrams (IDs)
extend Bayesian networks with decision variables and utility functions to
model the interaction between an agent and a system. The standard task is
to compute the maximum expected utility (MEU) and optimal policies\, one
of the most challenging tasks in graphical models. Computing upper bounds
on the MEU is desirable because they can guide search or sampling-based me
thods. In this talk\, I present bounding schemes for solving IDs: one exte
nds variational decomposition bounds in marginal MAP\, and another is a ne
w submodel tree decomposition method. Empirical results show these bounds
are orders of magnitude tighter than previous methods.\n\nhttps://cml.ics.
uci.edu/seminars/2021-02-08-junkyu-lee
X-ALT-DESC;FMTTYPE=text/html:Junkyu Lee\, AI Planning Gr
oup\, IBM Research
Title: Decomposition Bounds for Influence
Diagrams
Abstract: Influence diagrams (IDs) extend Bayesian
networks with decision variables and utility functions to model the inter
action between an agent and a system. The standard task is to compute the
maximum expected utility (MEU) and optimal policies\, one of the most chal
lenging tasks in graphical models. Computing upper bounds on the MEU is de
sirable because they can guide search or sampling-based methods. In this t
alk\, I present bounding schemes for solving IDs: one extends variational
decomposition bounds in marginal MAP\, and another is a new submodel tree
decomposition method. Empirical results show these bounds are orders of ma
gnitude tighter than previous methods.
https://cml.ics.uci.edu/seminars/2021
-02-08-junkyu-lee
URL:https://cml.ics.uci.edu/seminars/2021-02-08-junkyu-lee
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