Jeff S. Shamma
Aspiration Learning in Coordination Games.
Fri, 10/21/2011 - 15:01 — SomNambulAuthors: Jeff S. Shamma, Georgios C. Chasparis, Ari Arapostathis
Subjects: Computer Science and Game Theory
AbstractWe consider the problem of distributed convergence to efficient outcomes in
coordination games through dynamics based on aspiration learning. Under
aspiration learning, a player continues to play an action as long as the
rewards received exceed a specified aspiration level. Here, the aspiration
level is a fading memory average of past rewards, and these levels also are
subject to occasional random perturbations.- Read more
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Multiple-Model Adaptive Control With Set-Valued Observers.
Tue, 09/08/2009 - 12:16 — SomNambulAuthors: Paulo Rosa, Carlos Silvestre, Jeff S. Shamma, Michael Athans
Subjects: Optimization and Control
AbstractThis paper proposes a multiple-model adaptive control methodology, using
set-valued observers (MMAC-SVO) for the identification subsystem, that is able
to provide robust stability and performance guarantees for the closed-loop,
when the plant, which can be open-loop stable or unstable, has significant
parametric uncertainty. We illustrate, with an example, how set-valued
observers (SVOs) can be used to select regions of uncertainty for the
parameters of the plant.Multiple-Model Adaptive Control With Set-Valued Observers.
Tue, 09/08/2009 - 12:16 — SomNambulAuthors: Paulo Rosa, Carlos Silvestre, Jeff S. Shamma, Michael Athans
Subjects: Optimization and Control
AbstractThis paper proposes a multiple-model adaptive control methodology, using
set-valued observers (MMAC-SVO) for the identification subsystem, that is able
to provide robust stability and performance guarantees for the closed-loop,
when the plant, which can be open-loop stable or unstable, has significant
parametric uncertainty. We illustrate, with an example, how set-valued
observers (SVOs) can be used to select regions of uncertainty for the
parameters of the plant.
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