Accession Number : AD1051572


Title :   Optimizing Human Input in Social Network Analysis


Descriptive Note : Technical Report,13 Jul 2016,12 Apr 2017


Corporate Author : University of Texas at Austin Austin United States


Personal Author(s) : Shakkottai,Sanjay


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1051572.pdf


Report Date : 23 Jan 2018


Pagination or Media Count : 67


Abstract : The study focused on developing new bandit algorithms for online optimization (e.g. matching tasks to human agents). The attached technical reports provide details on the formulations and results. Specifically, the technical reports focused on a backlog minimization formulation for matching tasks and agents, as well as a contextual bandit formulation focusing on dimensionality reduction.


Descriptors :   Social Networks , RANDOM variables , machine learning , information systems , probability , stochastic processes , information theory , generative models , algorithms , computational science , phase transformations , artificial intelligence , dimensionality reduction


Subject Categories : Information Science


Distribution Statement : APPROVED FOR PUBLIC RELEASE