PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

How Automated Agents Treat Humans and Other Automated Agents in Situations of Inequity: An Experimental Study
Ron Katz and Sarit Kraus
In: AAMAS-08, 12-16 May 2008, Estoril, Portugal.


This paper explores the question of how agent designers perceive and treat their agent’s opponents. In particular, it examines the in- fluence of the opponent’s identity (human vs. automated agent) in negotiations. We empirically demonstrate that when people interact spontaneously they treat human opponents differently than automated agents in the context of equity and fairness considerations. However, these difference vanish when people design and implement agents that will interact on their behalf. Nevertheless, the commitment of the agents to honor agreements with people is higher than their commitment to other agents. In the experiments, which comprised 147 computer science students, we used the Colored Trails game as the negotiation environment. We suggest possible explanations for the relationships among online players, agent designers, human opponents and automated opponents.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
ID Code:3843
Deposited By:Ron Katz
Deposited On:25 February 2008