Intelligent Tutoring System for Negotiation Skills Training

Johnson, Emmanuel; Lucas, Gale M.; Kim, Peter; Gratch, Jonathan

doi:10.1007/978-3-030-23207-8_23

Cited by 26 publications

(26 citation statements)

References 8 publications

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“…This provides a concrete structure to the negotiation and thus, it helps to keep the conversations focused and the evaluation of the negotiation performance tractable. Nevertheless, it can lead to a diverse set of dialogues, depending on whether the participant preferences align or not, making it suitable for a variety of applications such as in social skills training [6] or building artificial assistants [24]. In addition, the CaSiNo task is grounded in a real-world campsite scenario that promotes contextually-rich personal conversations in freeform natural language.…”

Section: Dataset and Methodsmentioning

confidence: 99%

“…Adding the participant's affect variables based on the T5-Emotion model in the second step, helps to account for a much higher variance (F(20, 1991)=10.41, p < .001, R 2 =.095), such that this increase in the proportion is itself highly significant (∆F(6, 1991)=26.02, p < .001, ∆R 2 =.071). Yet, further variance is explained when the partner's affect variables are incorporated (F (26,1985)=10.88, p < .001, R 2 =.125: ∆F (6,1985)=11.38, p < .001, ∆R 2 =.030).…”

Section: Regression Analysismentioning

confidence: 99%

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Towards Emotion-Aware Agents for Negotiation Dialogues

Chawla¹,

Clever²,

Ramirez³

et al. 2022

Preprint

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Negotiation is a complex social interaction that encapsulates emotional encounters in human decision-making. Virtual agents that can negotiate with humans are useful in pedagogy and conversational AI. To advance the development of such agents, we explore the prediction of two important subjective goals in a negotiation - outcome satisfaction and partner perception. Specifically, we analyze the extent to which emotion attributes extracted from the negotiation help in the prediction, above and beyond the individual difference variables. We focus on a recent dataset in chat-based negotiations, grounded in a realistic camping scenario. We draw extensive qualitative and quantitative comparisons between three types of emotion variables - emoticons, as well as lexical and contextual}variables, by leveraging affective lexicons and a state-of-the-art deep learning architecture. To further validate the findings, we analyze the prediction of these subjective negotiation goals after controlling for the objective performance of the participants. We also study the temporal effects, understanding the contribution of emotion expressed in the initial and latter parts of the conversation. Finally, we discuss our insights, which will be helpful for designing adaptive negotiation agents that interact through realistic communication interfaces.

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Section: Dataset and Methodsmentioning

confidence: 99%

Section: Regression Analysismentioning

confidence: 99%

Towards Emotion-Aware Agents for Negotiation Dialogues

Chawla¹,

Clever²,

Ramirez³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Using a developed set of metrics that can measure the quality of negotiation outcomes, researchers have determined that certain principles used to confirm positive negotiation outcomes in a human–human negotiation also indicate success in a human–agent negotiation. Using these metrics and comparing the impact of feedback framing on negotiation outcomes, Johnson et al (2019) showed that personalized feedback based on the user’s recent negotiation session is most effective at helping a student claim more value. The researchers were not as successful in teaching users how to create more value.…”

Section: Negotiation With Computer‐controlled Agents (Bots)mentioning

confidence: 99%

Artificial Intelligence and Technology in Teaching Negotiation

Dinnar¹,

Dede²,

Johnson³

et al. 2021

Negotiation Journal

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Artificial intelligence (AI), machine learning (ML), affective computing, and big‐data techniques are improving the ways that humans negotiate and learn to negotiate. These technologies, long deployed in industry and academic research, are now being adopted for educational use. We describe several systems that help human negotiators evaluate and learn from role‐play simulations as well as applications that help human instructors teach negotiators at the individual, team, and organizational levels. AI can enable the personalization of negotiation instruction, taking into consideration factors such as culture and bias. These tools will enable improvements not only in the teaching of negotiation, but also in teaching humans how to program and collaborate with technology‐based negotiation systems, including avatars and computer‐controlled negotiation agents. These advances will provide theoretical and practical insights, require serious consideration of ethical issues, and revolutionize the way we practice and teach negotiation.

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“…Work in our lab has shown that algorithms can automatically diagnose specific errors that novice negotiators make (Johnson, Roediger et al 2019) and provide feedback tailored to those specific weaknesses. Studies have demonstrated that students improve their performance in subsequent automated negotiations (Monahan et al 2018; Johnson, Lucas et al 2019), though research is still needed to see if these benefits generalize to interactions with other people.…”

Section: Automated Negotiators As Online Tutorsmentioning

confidence: 99%

The Promise and Peril of Automated Negotiators

Gratch¹

2021

Negotiation Journal

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Innovations in artificial intelligence are enabling a new class of applications that can negotiate with people through chat or spoken language. Developed in close collaboration with behavioral science research, these algorithms can detect, mimic, and leverage human psychology, enabling them to undertake such functions as the detection of common mistakes made by novice negotiators. These algorithms can simulate the cognitive processes that shape human negotiations and make use of these models to influence negotiated outcomes. This article reviews some of the scientific advances enabling this technology and discusses how it is being used to advance negotiation research, teaching, and practice.

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Intelligent Tutoring System for Negotiation Skills Training

Cited by 26 publications

References 8 publications

Towards Emotion-Aware Agents for Negotiation Dialogues

Towards Emotion-Aware Agents for Negotiation Dialogues

Artificial Intelligence and Technology in Teaching Negotiation

The Promise and Peril of Automated Negotiators

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