Academia.edu no longer supports Internet Explorer.
To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser.
We introduce an emotional agent model that shows how emotions affect an agent's nego-tiation strategy. By adding emotions, we add the effects of these indirectly related fea-tures to the negotiation, features that are ig-nored in most models. Our new method, the PAD Emotional Negotiation Model, maps a nonemotional agent's strategy during nego-tiation to the strategy used by an emotional agent. Our evaluations show this model can be used to implement agents with various emotional states that mimic human emotions during negotiation.
ArXiv, 2021
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 study three degrees of emotion dimensions – emoticons, lexical, and contextual by leveraging affective lexicons and a state-of-the-art deep learning architecture. Our insights will be helpful in designing adaptive negotiation agents that interact through realistic communication interfaces.
2011
Social intelligence is the ability of manage and improve one's relation with others which can lead to better joint performance in a group. Social intelligence can improve agents' interaction in a multi-agent system. Negotiation is a multi-agent system in which agents are cooperating to reach a joint agreement as well as gain more individual utility. This situation has great potential for conflict between social and individual goals. In this paper, in order to compromise to reach both social and individual goals, emotional-social agents are proposed to manage negotiation interactions. Experimental results show that emotional-social negotiator agents reach fair agreement, and also achieve more individual gain.
2021
Negotiation is one of the crucial processes for resolving conflicts between parties. In automated negotiation, agent designers mostly take opponent’s offers and the remaining time into account while designing their strategies. While designing a negotiating agent interacting with a human directly, other information such as opponent’s emotional changes during the negotiation can establish a better interaction and reach an admissible settlement for joint interests. Accordingly, this paper proposes a bidding strategy for humanoid robots, which incorporates their opponents’ emotional states and awareness of the agent’s changing behavior.
IFSA World Congress and …, 2001
Proceedings of the …, 2009
Negotiation is a daily occurring process at all levels of society. Emotion plays an important role in negotiation between humans. In this paper we discuss to what extent and in which form ICT techniques can be used to get a grip on the emotional processes that play a role during negotiations. We focus on emotion recognition and measurement. Our analysis shows that current emotion recognition & measurement technology is mainly usable in the preparation for negotiations (including training sessions) and during offline moments of the negotiation (e.g., time-outs). The main arguments for this conclusion are: (1) valid and reliable emotion recognition and measurement techniques are usually invasive, and, (2) it is unclear if participants in a negotiation accept the technology.
CHI '07 Extended Abstracts on Human Factors in Computing Systems, 2007
This paper investigates the manner in which decisionmaking is influenced by the impressions given by lifelike agents in negotiation situations. These impressions comprise an agent's facial expressions such as happy and sad, and the history of relationship with the agent. In this paper, we introduce a negotiation game as one of the basic interactions based on the soft game theory. The experimental results reveal that expressions and history significantly influence the receiver's impressions and decision-making. The findings of this study can be beneficial for designing the nonverbal expressions of an animated agent who can negotiate and make a deal with users.
Valid interpretation of the nonverbal behavior of the people involved in negotiations is important. Computational agents that are designed for negotiation benefit from the ability to interpret human nonverbal behavior for communicating more effectively and achieving their goals. In this paper, we demonstrate how the mode of involvement and relational affect of the negotiators involved in the interaction can be determined by several nonverbal behaviors such as that of the mouth, head, hand movements, posture and the facial expressions of the negotiators. We use machine learning to study involvement and affect in negotiation. Our results show that the prediction models built based on nonverbal cues can help identify the negotiator's attitudes and motivation in the interaction.
Modelling real persons or virtual agents motivations, personality and emotions is a key feature of many useroriented applications. Most of the previous work has defined rich cognitive models of motivations, personality and emotions, but have relied on some kind of reactive scheme of problem solving and execution. Instead, this work proposes a deliberative emotional model for virtual agents based in their basic needs, preferences and personality traits. More specifically, we advocate the integration of these comprehensive agents models within deliberative automated planning techniques, so that plans to be executed by agents to achieve their goals already incorporate reasoning at the emotional level.
International Journal of Intelligent Information Technologies, 2009
Prior research on negotiation support systems (NSS) has paid limited attention to the information content in the observed bid sequences of negotiators as well as on the cognitive limitations of individual negotiators and their impacts on negotiation performance. In this paper, we assess the performance of human subjects in the context of agent-based NSS, and the accuracy of an exponential functional form in representing observed human bid sequences. We then predict the reservation values of negotiators based on their observed bids. Finally, we study the impact of negotiation support systems in helping users realize superior negotiation outcomes. Results indicate that an exponential function is a good model for observed bids.
Computational Intelligence, 1995
2011
ABSTRACT There is now considerable evidence in social psychology, economics, and related disciplines that emotion plays an important role in negotiation. For example, humans make greater concessions in negotiation to an opposing human who expresses anger, and they make fewer concessions to an opponent who expresses happiness, compared to a no-emotion-expression control.
2020
Social agents within Intelligent Learning Environments can enhance their own abilities by interacting to reach agreements and carrying them out. The typical reasoning process that directs agents to rejection or acceptance of these agreements is negotiation. In broad terms, an agreement is a series of conditions and commitments accepted by the parties involved that may refer to a future action plan, an exchange of articles, or assignment of tasks and roles. This article is aimed at proposing a negotiating agent that manifests realistic reactions resulting from forming emotional relationships that reflect in their interactions the characteristics of their personality and their emotional state. To this end, the use of negotiations in the development of social activities, some approaches for the implementation of negotiations between agents and software for the implementation of these agents are addressed. To conclude, the proposal of an agent that includes personality and emotions in t...
arXiv: Human-Computer Interaction, 2020
This document describes our agent Pilot, winner of the Human-Agent Negotiation Challenge at ANAC, IJCAI 2020. Pilot is a virtual human that participates in a sequence of three negotiations with a human partner. Our system is based on the Interactive Arbitration Guide Online (IAGO) negotiation framework. We leverage prior Affective Computing and Psychology research in negotiations to guide various key principles that define the behavior and personality of our agent.
Negotiation Journal
Recognition of the role played by emotions in negotiation is growing. This article synthesizes current research around four broad themes: moves and exchanges, information processing, social interaction, and context. The authors' review reveals that much of the research on this topic has focused on two key emotions, anger and happiness. More recently, negotiators have turned to other emotions such as guilt and disappointment, demonstrating that not all negative emotions have the same consequences, or activate the same regions of the brain. Focusing on social interaction, the authors note that negotiators may influence each others' emotions: whether negotiators converge to anger or happiness has different consequences for agreement. Researchers have broadened their examination of emotion by considering how external factors such as power, the number of negotiators, culture, and gender influence the impact of emotional expression. The authors also consider the function and impac...
Studies in Computational Intelligence, 2011
Motivated by the challenges of bilateral negotiations between people and automated agents we organized the first automated negotiating agents competition (ANAC 2010). The purpose of the competition is to facilitate the research in the area bilateral multi-issue closed negotiation. The competition was based on the Genius environment, which is a General Environment for Negotiation with Intelligent multi-purpose Usage Simulation. The first competition was held in conjunction with the Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS-10) and was comprised of seven teams. This paper presents an overview of the competition, as well as general and contrasting approaches towards negotiation strategies that were adopted by the participants of the competition. Based on analysis in post-tournament experiments, the paper also attempts to provide some insights with regard to effective approaches towards the design of negotiation strategies.
To date, a variety of automated negotiation agents have been created. While each of these agents has been shown to be effective in negotiating with people in specific environments, they lack natural language processing support required to enable real-world types of interactions. In this paper we present NegoChat, the first negotiation agent that successfully addresses this limitation. NegoChat contains several significant research contributions. First, we found that simply modifying existing agents to include an NLP module is insufficient to create these agents. Instead, the agents' strategies must be modified to address partial agreements and issue-byissue interactions. Second, we present NegoChat's negotiation algorithm. This algorithm is based on bounded rationality, and specifically Aspiration Adaptation Theory (AAT). As per AAT, issues are addressed based on people's typical urgency, or order of importance. If an agreement cannot be reached based on the value the human partner demands, the agent retreats, or downwardly lowers the value of previously agreed upon issues so that a "good enough" agreement can be reached on all issues. This incremental approach is fundamentally different from all other negotiation agents, including the state-of-the-art KBAgent. Finally, we present a rigorous evaluation of NegoChat, showing its effectiveness.
To date, a variety of automated negotiation agents have been created. While each of these agents has been shown to be effective in negotiating with people in specific environments, they lack natural language processing (NLP) methods required to enable real-world types of interactions. In this paper we study how existing agents must be modified to address this limitation. After performing an extensive study of agents' negotiation with human subjects, we found that simply modifying existing agents to include an NLP module is insufficient to create these agents. Instead the agents' strategies must be modified to address partial agreements and issue-by-issue interactions.
2009
Research on automated negotiators has flourished in recent years. Yet, most of the time, the research does not focus on automated negotiators capable of negotiating efficiently with people. Many challenges are facing agents designers who aim to design an automated negotiator, even when people are not in the loop. In addition, the fact that people are scarce resources makes the validation process of the efficacy of the automated negotiators an exhausting task. Yet, these challenges can be overcome. By reviewing existing automated negotiators and reporting on experiments we conducted with automated negotiators, we shed some light on how these challenges can be overcome, and thus motivate other researchers to pursue this exciting line of research.
European Journal of Operational Research, 1990
The negotiation problem representation in evolutionary systems design ) is interpreted to include social-emotional as well as task aspects. Controls are actions having task and social-emotional components taken to deliver preferred combinations of task and social-emotional goals. Thus, normative controls (actions) recommended by a group decision and negotiation support system (GDNSS) such as MEDXATOR can include both task and social-emotional components. We use as controls the categories of social interaction developed by , as well as the interaction rate. We study empirically the relation between these controls and the agreements reached by negotiators in a buyer-seller negotiation. The role of social-emotional interaction in the negotiation process and thus in its support by GDNSS is analytically specified.
Cognitive Technologies, 2012
The development of proficient automated agents has flourished in recent years, yet making the agents interact with people has still received little attention. This is mainly due to the unpredictable nature of people and their negotiation behavior, though complexity and costs attached to experimentation with people, starting from the design and ending with the evaluation process, is also a factor. Even so, succeeding in designing proficient automated agents remains an important objective. In recent years we have invested much effort in facilitating the design and evaluation of automated agents interacting with people, making it more accessible to researchers. We have created two distinct environments for bargaining agents, as well as proposing a novel approach for evaluating agents. These are key factors for making automated agents become a reality rather than remain theoretical.
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.