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Automated Negotiation with Humans.
~
Nazari, Zahra.
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Automated Negotiation with Humans.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Automated Negotiation with Humans./
作者:
Nazari, Zahra.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:
109 p.
附註:
Source: Dissertations Abstracts International, Volume: 80-06, Section: B.
Contained By:
Dissertations Abstracts International80-06B.
標題:
Artificial intelligence. -
電子資源:
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.
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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.
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