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<bibitem type="C">   <ARLID>0396808</ARLID> <utime>20240103203028.3</utime><mtime>20131009235959.9</mtime>         <title language="eng" primary="1">On Approximate Fully Probabilistic Design of Decision-Making Units</title>  <specification> <page_count>12 s.</page_count> <media_type>P</media_type> </specification>   <serial><ARLID>cav_un_epca*0396446</ARLID><ISBN>978-80-903834-8-7</ISBN><title>Preprints of the  3rd International Workshop on Scalable Decision Making held in conjunction with ECML/PKDD 2013</title><part_num/><part_title/><publisher><place>Prague</place><name>Institute of Information Theory and Automation</name><year>2013</year></publisher><editor><name1>Guy</name1><name2>Tatiana V.</name2></editor><editor><name1>Kárný</name1><name2>Miroslav</name2></editor></serial>    <keyword>decision making</keyword>   <keyword>Bayesian learning</keyword>   <keyword>minimum cross-entropy principle</keyword>   <keyword>fully probabilistic design of DM strategies</keyword>   <keyword>linear-quadratic DM</keyword>    <author primary="1"> <ARLID>cav_un_auth*0101124</ARLID> <name1>Kárný</name1> <name2>Miroslav</name2> <full_dept language="cz">Adaptivní systémy</full_dept> <full_dept language="eng">Department of Adaptive Systems</full_dept> <department language="cz">AS</department> <department language="eng">AS</department> <institution>UTIA-B</institution> <full_dept>Department of Adaptive Systems</full_dept>  <fullinstit>Ústav teorie informace a automatizace AV ČR, v. v. i.</fullinstit> </author>   <source> <url>http://library.utia.cas.cz/separaty/2013/AS/karny-on approximate fully probabilistic design of decision-making units.pdf</url> </source>        <cas_special> <project> <project_id>GA13-13502S</project_id> <agency>GA ČR</agency> <ARLID>cav_un_auth*0292725</ARLID> </project>  <abstract language="eng" primary="1">An efficient support of a single decision maker is vital in  constructing scalable systems addressing complex decision-making (DM)  tasks. Fully probabilistic design (FPD) of DM strategies, an extension  of dynamic Bayesian DM, provides a firm basis for such a support.  The limited cognitive and evaluation resources of the supported decision  maker cause that theoretically optimal solutions are realised only approximately.  Thus, the truly efficient support has to include reliable means  for constructing approximate solutions of DM subtasks. The current paper  deals with the design of the approximately optimal DM strategy for  a known environment model and adequately described DM preferences.  The design relies on: a) the explicit minimiser found within FPD; b)  randomised nature of the strategy provided by FPD.</abstract>  <action target="WRD"> <ARLID>cav_un_auth*0294526</ARLID> <name>The 3rd International Workshop on Scalable Decision Making: Uncertainty, Imperfection, Deliberation held in conjunction with ECML/PKDD 2013</name>  <place>Prague</place> <dates>23.09.2013-23.09.2013</dates>  <country>CZ</country> </action>    <reportyear>2014</reportyear>  <RIV>BB</RIV>      <num_of_auth>1</num_of_auth>  <presentation_type> PO </presentation_type> <inst_support> RVO:67985556 </inst_support>  <permalink>http://hdl.handle.net/11104/0224702</permalink>  <unknown tag="mrcbC61"> 1 </unknown>       <arlyear>2013</arlyear>       <unknown tag="mrcbU63"> cav_un_epca*0396446 Preprints of the  3rd International Workshop on Scalable Decision Making held in conjunction with ECML/PKDD 2013 978-80-903834-8-7 Preprints of the  3rd International Workshop on Scalable Decision Making held in conjunction with ECML/PKDD 2013 Prague Institute of Information Theory and Automation 2013 </unknown> <unknown tag="mrcbU67"> Guy Tatiana V. 340 </unknown> <unknown tag="mrcbU67"> Kárný Miroslav 340 </unknown> </cas_special> </bibitem>