Sequential Stochastic Decision Processes II
{'Code': '07010000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'ECCS', 'LongName': 'Div Of Electrical, Commun & Cyber Sys'}}
{'SignBlockName': 'Radhakisan Baheti', 'PO_EMAI': None, 'PO_PHON': None}
The project described in this proposal consists of five research        issues in the general area of Sequential Stochastic Decision Processes.         Four of the issues focus on two established lines of research in Markov         Decision Processes and Sequential Stochastic Games, respectively.  For          the former class of models an approach to the analysis of the "risk-            sensitive" versions of these models is suggested.  For the latter class,        the project focusses on the issues of characterization and computation of       "stationary equilibrium strategies".  For both classes of models,               successful resolution of these research issues would be likely to               increase their appeal as modeling tools in applications.  Furthermore,          the first research issue proposes a method of embedding the famous              "Traveling Salesman Problem" in an appropriately constructed (and then          perturbed) Markov Decision Process.  It is expected that this new way of        viewing the traveling Salesman Problem will lead to useful computational        procedures and theoretical insights.  An important objective of this            project is the demonstration that all of the above research issues can be       analyzed with the help of a conceptually unified approach.  Namely, by          formulating the respective subproblems as mathematical programs which in        turn should be solved by a combination of mathematical programming              techniques, and the relevant dynamic programming/game theoretic                 techniques.                                                                             This research concerns certain types of sequential stochastic           decision processes:  such processes are used in modeling a wide variety         of engineering problems in manufacturing, power systems and economics.          An objective of this project is to show that numerous research issues           associated with sequential stochastic decision processes can be analyzed        by formulating them as mathematical programs.  It is expected that this         research will lead to better algorithms for solving these problems.
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{'Name': 'University of Maryland Baltimore County', 'CityName': 'BALTIMORE', 'ZipCode': '212500001', 'PhoneNumber': '4104553140', 'StreetAddress': '1000 HILLTOP CIR', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Maryland', 'StateCode': 'MD', 'CONGRESSDISTRICT': '07', 'CONGRESS_DISTRICT_ORG': 'MD07', 'ORG_UEI_NUM': 'RNKYWXURFRL5', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF MARYLAND BALTIMORE COUNTY', 'ORG_PRNT_UEI_NUM': 'KMGEN62NKEM9'}
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[{'Code': '1454', 'Text': 'SYSTEMS THEORY'}, {'Code': '2389', 'Text': None}]
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8704954.xml'}