Challenges and Main Results of the Automated Negotiating Agents Competition (ANAC) 2019

Aydoğan, Reyhan; Baarslag, Tim; Fujita, Katsuhide; Mell, Johnathan; Gratch, Jonathan; Jonge, Dave de; Mohammad, Yasser; Nakadai, Shinji; Morinaga, Satoshi; Osawa, Hirotaka; Aranha, Claus; Jonker, Catholijn M.

doi:10.1007/978-3-030-66412-1_23

Cited by 23 publications

(26 citation statements)

References 32 publications

(34 reference statements)

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“…Our work suggests that demographic and personality attributes inferred from past social interactions (such as on social media) are not sufficient and that providing information to negotiating agents from affective channels tracked during the negotiation itself is important for developing agents that can predict or understand if their human counterpart is satisfied and likes them. Ultimately, outcomes like satisfaction and liking will be essential for such agents to cultivate if they, like human negotiators, hope to successfully negotiate with that same partner in future interactions [4].…”

Section: Discussionmentioning

confidence: 99%

“…Oliver and colleagues studied how profit expectations relate to satisfaction after the bargaining in buyerseller interactions, finding that higher expectations had the effect of decreased satisfaction [2]. Maintaining a positive relationship with the partner is especially crucial in repeated interactions, where poor relations in earlier negotiations can adversely impact the results of future ones [4]. Relationship also manifests in the context of rapport building [21], favor exchange [22], and reputation effects [23].…”

Section: Related Workmentioning

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: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Towards Emotion-Aware Agents for Negotiation Dialogues

Chawla¹,

Clever²,

Ramirez³

et al. 2022

Preprint

Self Cite

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“…and 2) 5-point scale for opponent likeness (How much do you like your opponent?). Back-to-back negotiation (Aydogan et al, 2020) is an interesting case where the relationship with the partner is crucial. In such a case, a poor relationship in earlier negotiations can adversely impact the performance in later rounds.…”

Section: Conversationmentioning

confidence: 99%

CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems

Chawla¹,

Ramirez²,

Clever³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Automated systems that negotiate with humans have broad applications in pedagogy and conversational AI. To advance the development of practical negotiation systems, we present CaSiNo: a novel corpus of over a thousand negotiation dialogues in English. Participants take the role of campsite neighbors and negotiate for food, water, and firewood packages for their upcoming trip. Our design results in diverse and linguistically rich negotiations while maintaining a tractable, closeddomain environment. Inspired by the literature in human-human negotiations, we annotate persuasion strategies and perform correlation analysis to understand how the dialogue behaviors are associated with the negotiation performance. We further propose and evaluate a multi-task framework to recognize these strategies in a given utterance. We find that multi-task learning substantially improves the performance for all strategy labels, especially for the ones that are the most skewed. We release the dataset, annotations, and the code to propel future work in human-machine negotiations: https:// github.com/kushalchawla/CaSiNo.

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“…A negotiator that tries to take too much can annoy their partner, and in turn, hurt their likeability in the eyes of their partners and also the partner's affective evaluation of the outcome (that is, their satisfaction). Instead, it is desirable for the negotiator to strive for maximum performance while ensuring that the partner is satisfied [2] and leaves with a positive perception of the partner [3,4]. Therefore, predicting the partner's satisfaction and perception in advance can be crucial for an AI assistant that aims to negotiate with its users.…”

Section: Introductionmentioning

confidence: 99%