國立虎尾科技大學 |

Modeling, learning and reasoning about preference trees over combinatorial domains.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Modeling, learning and reasoning about preference trees over combinatorial domains./
Author:	Liu, Xudong.
Description:	1 online resource (161 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 77-10(E), Section: B.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9781339730769

Modeling, learning and reasoning about preference trees over combinatorial domains.
Liu, Xudong.

Modeling, learning and reasoning about preference trees over combinatorial domains. - 1 online resource (161 pages)

Source: Dissertation Abstracts International, Volume: 77-10(E), Section: B.

Thesis (Ph.D.)--University of Kentucky, 2016.

Includes bibliographical references

In my Ph.D. dissertation, I have studied problems arising in various aspects of preferences: preference modeling, preference learning, and preference reasoning, when preferences concern outcomes ranging over combinatorial domains. Preferences is a major research component in artificial intelligence (AI) and decision theory, and is closely related to the social choice theory considered by economists and political scientists. In my dissertation, I have exploited emerging connections between preferences in AI and social choice theory. Most of my research is on qualitative preference representations that extend and combine existing formalisms such as conditional preference nets, lexicographic preference trees, answer-set optimization programs, possibilistic logic, and conditional preference networks; on learning problems that aim at discovering qualitative preference models and predictive preference information from practical data; and on preference reasoning problems centered around qualitative preference optimization and aggregation methods. Applications of my research include recommender systems, decision support tools, multi-agent systems, and Internet trading and marketing platforms.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781339730769Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Modeling, learning and reasoning about preference trees over combinatorial domains.
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100 1 $a Liu, Xudong. $3 1186547
245 1 0 $a Modeling, learning and reasoning about preference trees over combinatorial domains.
264 0 $c 2016
300 $a 1 online resource (161 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Dissertation Abstracts International, Volume: 77-10(E), Section: B.
500 $a Adviser: Miroslaw Truszczynski.
502 $a Thesis (Ph.D.)--University of Kentucky, 2016.
504 $a Includes bibliographical references
520 $a In my Ph.D. dissertation, I have studied problems arising in various aspects of preferences: preference modeling, preference learning, and preference reasoning, when preferences concern outcomes ranging over combinatorial domains. Preferences is a major research component in artificial intelligence (AI) and decision theory, and is closely related to the social choice theory considered by economists and political scientists. In my dissertation, I have exploited emerging connections between preferences in AI and social choice theory. Most of my research is on qualitative preference representations that extend and combine existing formalisms such as conditional preference nets, lexicographic preference trees, answer-set optimization programs, possibilistic logic, and conditional preference networks; on learning problems that aim at discovering qualitative preference models and predictive preference information from practical data; and on preference reasoning problems centered around qualitative preference optimization and aggregation methods. Applications of my research include recommender systems, decision support tools, multi-agent systems, and Internet trading and marketing platforms.
520 $a KEYWORDS: preferences, decision theory, social choice theory, knowledge representation and reasoning, computational complexity, artificial intelligence.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Kentucky. $3 1184531
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10109807 $z click for full text (PQDT)