A Computational Model of Power in Collaborative Negotiation Dialogues

Ouali, Lydia Ould; Rich, Charles

doi:10.1007/978-3-319-67401-8_35

Cited by 4 publications

(1 citation statement)

References 20 publications

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“…The AI community has developed a number of algorithms to play the multi-issue bargaining task, either with other agents (e.g., [18]) or with humans [21][22][23]. In this paper, we explore extensions to the popular IAGO framework [16], and specifically the "Pinocchio" agent that is provided by default [24].…”

Section: Iago Extensionsmentioning

confidence: 99%

The Impact of Implicit Information Exchange in Human-agent Negotiations

Johnson

Gratch

2020

Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents

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Intelligent virtual agents have been developed to study, assess and teach a variety of human interpersonal skills. Here we examine the impact of an agent's perspective-taking sophistication on human negotiators. Good perspective-takers can discover creative solutions that benefit both parties, but many have difficulty with this skill. In particular, novices focus on explicit goal-statements (e.g., "I want apples more than bananas") but discount goal-relevant information implicit in the opponent's offers. Many human-agent negotiation agents similarly ignore implicit information. We examined the influence of implicit information on human negotiators by independently enhancing agents in two ways: do agents communicate implicit information and do they attend to implicit information communicated by users. We find that communicating implicit information seems to confuse user's perspective-taking ability, yet paradoxically, helps lead them to better outcomes. In contrast, an agent that attends to user's implicit communications shows better perspective-taking but fails to translate this into better outcomes. These results emphasize the challenges associated with implicit information. We discuss how these results impact the design of negotiation agents for applications, analysis and pedagogy.

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Section: Iago Extensionsmentioning

confidence: 99%

The Impact of Implicit Information Exchange in Human-agent Negotiations

Johnson

Gratch

2020

Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents

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Guess My Power: A Computational Model to Simulate a Partner’s Behavior in the Context of Collaborative Negotiation

Ouali

Rich

2018

Advances in Intelligent Systems and Computing

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Hybrid intelligence in hospitals: towards a research agenda for collaboration

2021

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Successful collaboration between clinicians is particularly relevant regarding the quality of care process. In this context, the utilization of hybrid intelligence, such as conversational agents (CAs), is a reasonable approach for the coordination of diverse tasks. While there is a great deal of literature involving collaboration, little effort has been made to integrate previous findings and evaluate research when applying CAs in hospitals. By conducting an extended and systematic literature review and semi-structured expert interviews, we identified four major challenges and derived propositions where in-depth research is needed: 1) audience and interdependency; 2) connectivity and embodiment; 3) trust and transparency; and 4) security, privacy, and ethics. The results are helpful for researchers as we discuss directions for future research on CAs for collaboration in a hospital setting enhancing team performance. Practitioners will be able to understand which difficulties must be considered before the actual application of CAs.

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A Computational Model of Power in Collaborative Negotiation Dialogues

Cited by 4 publications

References 20 publications

The Impact of Implicit Information Exchange in Human-agent Negotiations

The Impact of Implicit Information Exchange in Human-agent Negotiations

Guess My Power: A Computational Model to Simulate a Partner’s Behavior in the Context of Collaborative Negotiation

Hybrid intelligence in hospitals: towards a research agenda for collaboration

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