東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Automated Negotiation with Humans.

Nazari, Zahra.

Linked to FindBook

Google Book

Amazon

博客來

Automated Negotiation with Humans.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Automated Negotiation with Humans./
Author:	Nazari, Zahra.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	109 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-06, Section: B.
Contained By:	Dissertations Abstracts International80-06B.
Subject:	Artificial intelligence. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=11016178

Automated Negotiation with Humans.
Nazari, Zahra.

Automated Negotiation with Humans. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 109 p.

Source: Dissertations Abstracts International, Volume: 80-06, Section: B.

Thesis (Ph.D.)--University of Southern California, 2017.

This item must not be sold to any third party vendors.

Negotiation is a crucial skill in personal and organizational interactions. In the last two decades, there has been a growing interest to create agents that can autonomously negotiate with other agents. The focus of this thesis, however, is on creating agents that can negotiate with human opponents. Besides improving on artificial social intelligence, such agents could be used for the purpose of training or assisting human negotiators. A central challenge is to handle the complexity of actual human behavior. When compared with idealized game-theoretic models, human negotiations are far richer, both in terms of the nature of information exchanged and the number of factors that inform their decision-making. We consider a negotiation task that is simple, yet general enough to drive agent-human research, and analyze an extensive data set of transcribed human negotiation on such tasks. Based on human behavior in this task, and the previous research on human negotiations, we propose a new framework to structure the design of agents that negotiate with people. We address two main decision problems inspired by this framework: modeling and influencing the opponent. Three techniques are proposed to model an opponent's preferences and character (e.g. honesty and personality traits) and a misrepresentation technique are then used to influence the opponent and gain better profit. The proposed techniques are then implemented in automatic web-based agents. We ran a number of negotiations between these agents and humans recruited on Amazon Mechanical Turk. The resulting data show that the agents can perform these strategies successfully when negotiating with human counterparts and give us valuable insight into the behavior of humans when negotiating with an agent.Subjects--Topical Terms:

516317
Artificial intelligence.

Automated Negotiation with Humans.
LDR:02789nmm a2200313 4500 001 2266710
005 20200624083554.5
008 220629s2017 ||||||||||||||||| ||eng d
035 $a (MiAaPQ)AAI11016178
035 $a (MiAaPQ)US_Calif_461175
035 $a AAI11016178
040 $a MiAaPQ $c MiAaPQ
100 1 $a Nazari, Zahra. $3 3543926
245 1 0 $a Automated Negotiation with Humans.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 109 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-06, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Gratch, Jonathan.
502 $a Thesis (Ph.D.)--University of Southern California, 2017.
506 $a This item must not be sold to any third party vendors.
520 $a Negotiation is a crucial skill in personal and organizational interactions. In the last two decades, there has been a growing interest to create agents that can autonomously negotiate with other agents. The focus of this thesis, however, is on creating agents that can negotiate with human opponents. Besides improving on artificial social intelligence, such agents could be used for the purpose of training or assisting human negotiators. A central challenge is to handle the complexity of actual human behavior. When compared with idealized game-theoretic models, human negotiations are far richer, both in terms of the nature of information exchanged and the number of factors that inform their decision-making. We consider a negotiation task that is simple, yet general enough to drive agent-human research, and analyze an extensive data set of transcribed human negotiation on such tasks. Based on human behavior in this task, and the previous research on human negotiations, we propose a new framework to structure the design of agents that negotiate with people. We address two main decision problems inspired by this framework: modeling and influencing the opponent. Three techniques are proposed to model an opponent's preferences and character (e.g. honesty and personality traits) and a misrepresentation technique are then used to influence the opponent and gain better profit. The proposed techniques are then implemented in automatic web-based agents. We ran a number of negotiations between these agents and humans recruited on Amazon Mechanical Turk. The resulting data show that the agents can perform these strategies successfully when negotiating with human counterparts and give us valuable insight into the behavior of humans when negotiating with an agent.
590 $a School code: 0208.
650 4 $a Artificial intelligence. $3 516317
650 4 $a Computer science. $3 523869
690 $a 0800
690 $a 0984
710 2 $a University of Southern California. $3 700129
773 0 $t Dissertations Abstracts International $g 80-06B.
790 $a 0208
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=11016178