88,727 research outputs found
New and Developing Research on Disparities in Discipline
This briefing paper describes the results of new research in the area of disciplinary disparities, and identifies remaining gaps in the literature that can guide researchers and funders of research. The brief is organized into two sections:1) What Have we Learned? Key New Research Findings describes research from leading scholars across the nation commissioned by The Center for Civil Rights Remedies at UCLA's Civil Rights Project with the support of the Collaborative, findings from projects supported by the Collaborative Funded Research Grant Program, and other new research on disproportionality in school discipline in the peer-reviewed literature.2) Future Research Needs describes gaps that remain in the research base. Although there has been considerable new knowledge generated in recent years, significant gaps remain, especially in identifying and evaluating intervention strategies that reduce inequity in discipline for all students
Exploiting Anonymity in Approximate Linear Programming: Scaling to Large Multiagent MDPs (Extended Version)
Many exact and approximate solution methods for Markov Decision Processes
(MDPs) attempt to exploit structure in the problem and are based on
factorization of the value function. Especially multiagent settings, however,
are known to suffer from an exponential increase in value component sizes as
interactions become denser, meaning that approximation architectures are
restricted in the problem sizes and types they can handle. We present an
approach to mitigate this limitation for certain types of multiagent systems,
exploiting a property that can be thought of as "anonymous influence" in the
factored MDP. Anonymous influence summarizes joint variable effects efficiently
whenever the explicit representation of variable identity in the problem can be
avoided. We show how representational benefits from anonymity translate into
computational efficiencies, both for general variable elimination in a factor
graph but in particular also for the approximate linear programming solution to
factored MDPs. The latter allows to scale linear programming to factored MDPs
that were previously unsolvable. Our results are shown for the control of a
stochastic disease process over a densely connected graph with 50 nodes and 25
agents.Comment: Extended version of AAAI 2016 pape
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