Can automated agents proficiently negotiate with humans?

Lin, Raz; Kraus, Sarit

doi:10.1145/1629175.1629199

Cited by 126 publications

(71 citation statements)

References 26 publications

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“…storage price). Also, the GOLEM agent platform [12] used to build our negotiation testbed RECON [2], has facilities that can include humans as avatars in the experiments (see [14] for an example of user immersed in an e-retailing agent environment), so our approach is generalisable for negotiations between humans and agents [41]. However, a detailed discussion of these issues is beyond the scope of this paper but covered by our plan for future work.…”

Section: Discussionmentioning

confidence: 99%

Concurrent bilateral negotiation for open e-markets: the Conan strategy

2017

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We develop a novel strategy that supports software agents to make decisions on how to negotiate for a resource in open and dynamic e-markets. Although existing negotiation strategies offer a number of sophisticated features, including modelling an opponent and negotiating with many opponents simultaneously, they abstract away from the dynamicity of the market and the model that the agent holds for itself in terms of ongoing negotiations, thus ignoring information that increases an agent's utility. Our proposed strategy COncurrent Negotiating AgeNts (Conan) considers a weighted combination of modelling the market environment and the progress of concurrent negotiations in which the agent partakes. We conduct extensive experiments to evaluate the strategy's performance in various settings where different opponents from the literature provide a competitive market. Our experiments provide statistically significant results showing how Conan outperforms the state-of-the-art in terms of the utility gained during negotiations.

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Section: Discussionmentioning

confidence: 99%

Concurrent bilateral negotiation for open e-markets: the Conan strategy

2017

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“…On the other hand, people's bargaining behavior does not adhere to equilibrium [3,9], and computers cannot use such strategies to negotiate well with people [8]. Our work shows that integrating opponent modeling and density estimation techniques is an effective approach for creating agents that can outperform people as well equilibrium strategies in revelation games.…”

Section: Related Workmentioning

confidence: 98%

“…Here, ω 1 * a,p is the optimal proposal for SIGAL in round 1, and Ea ω 1 * a,p | h 0 , ta is the expected benefit associated with this proposal, defined in Equation 8.…”

Section: Ea Respmentioning

confidence: 99%

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A study of computational and human strategies in revelation games

Peled

Gal

Kraus

2014

Auton Agent Multi-Agent Syst

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Revelation games are bilateral bargaining games in which agents may choose to truthfully reveal their private information before engaging in multiple rounds of negotiation. They are analogous to real-world situations in which people need to decide whether to disclose information such as medical records or university transcripts when negotiating over health plans and business transactions. This paper presents an agent-design that is able to negotiate proficiently with people in a revelation game with different dependencies that hold between players. The agent modeled the social factors that affect the players' revelation decisions on people's negotiation behavior. It was empirically shown to outperform people in empirical evaluations as well as agents playing equilibrium strategies. It was also more likely to reach agreement than people or equilibrium agents.

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“…Prior studies have been conducted to design the human-computer negotiation agent [5] [6], which demonstrate that a software agent can proficiently negotiate with and even outperform people. Here we illustrate some typical examples, such as the Diplomat agent [7], the AutONA agent [8], the Cliff-Edge Agent [9], the Colored-Trails agent [10], the Guessing Heuristic agent [11], the QOAgent [12], the Virtual Human agent [13], and the LaptopOnDemand.com [14].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Time-Dependent Strategy Model for Predicting Human’s Offer in E-Commerce Negotiation

Cao¹

2016

JSS

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Human-computer negotiation plays an important role in B2C e-commerce. There is a paucity of further scientific investigation and a pressing need on designing the software agent that can deal with the human's random and dynamic offer, which is crucially useful in human-computer negotiation to achieve better online negotiation outcomes. The lack of such studies has decelerated the process of applying automated negotiation to real world applications. To address the critical issue, this paper develops a dynamic time-dependent strategy concession model, that can predict the human's negotiation behavior during the process of the negotiation. To demonstrate the effectiveness of this model, we implement a prototype and conduct human-computer negotiations over 121 subjects. The experimental analysis not only confirms our model's effect but also reveals some insights into future work about human-computer negotiation systems.

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Can automated agents proficiently negotiate with humans?

Abstract: Exciting research in the design of automated negotiators is making great progress.

Cited by 126 publications

References 26 publications

Concurrent bilateral negotiation for open e-markets: the Conan strategy

Concurrent bilateral negotiation for open e-markets: the Conan strategy

A study of computational and human strategies in revelation games

Dynamic Time-Dependent Strategy Model for Predicting Human’s Offer in E-Commerce Negotiation

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