Be Selfish, But Wisely: Investigating the Impact of Agent Personality in Mixed-Motive Human-Agent Interactions (2023.emnlp-main)
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| Challenge: | A natural way to design a negotiation dialogue system is via self-play RL: train an agent that learns to maximize its performance by interacting with a simulated user that has been designed to imitate human-human dialogue data. |
| Approach: | They propose to use RL to train an agent that learns to maximize its performance by interacting with a simulated user that has been designed to imitate human-human dialogue data. |
| Outcome: | The proposed system fails to learn the value of compromise in a negotiation, which can lead to no agreements, and ultimately hurt the model's overall performance. |
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| Challenge: | Psychological evidence reveals the influence of personality traits on decision-making. |
| Approach: | They propose a simulation framework centered on large language model agents with synthesized personality traits and propose empirical insights into the strategic impacts of Big Five personality traits on outcomes of bilateral negotiations. |
| Outcome: | The proposed model can reproduce behavioral patterns observed in human negotiations. |
Strength Lies in Differences! Improving Strategy Planning for Non-collaborative Dialogues via Diversified User Simulation (2024.emnlp-main)
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| Challenge: | Non-collaborative dialogue agents are expected to engage in strategic conversations with diverse users, and this poses two main challenges for existing dialogue agents: 1) the inability to integrate user-specific characteristics into the strategic planning; 2) the difficulty of training strategic planners that can be generalized to diverse users. |
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| Outcome: | The proposed model can be used to achieve a mutual agreement that leans favorably towards the system's objectives. |
Evaluating and Enhancing the Robustness of Dialogue Systems: A Case Study on a Negotiation Agent (N19-1)
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| Challenge: | Recent research has demonstrated that goal-oriented dialogue agents can achieve striking performance when interacting with human users. |
| Approach: | They develop algorithms to evaluate the robustness of a goal-oriented dialogue agent by carefully designed attacks using adversarial agents. |
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Improving Dialog Systems for Negotiation with Personality Modeling (2021.acl-long)
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| Challenge: | In this paper, we introduce a framework for generating strategic dialog inspired by the idea of incorporating a theory of mind (ToM) into machines. |
| Approach: | They propose a probabilistic formulation to encapsulate the opponent's personality type during both learning and inference. |
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MADS: Multi-Agent Dialogue Simulation for Diverse Persuasion Data Generation (2025.emnlp-industry)
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| Challenge: | Recent studies show that LLM-based agents exhibit superior moral and emotional language performance compared to humans, raising expectations for their deployment in persuasive tasks. |
| Approach: | They propose a framework for generating persuasive multi-turn dialogues via agent self-play using user agents designed to simulate diverse persona-driven behaviors, a Dialog Agent executing task-oriented persuasion strategies and an Optimization Agent evaluating and refining dialogue outcomes. |
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Rethinking Personality Assessment from Human-Agent Dialogues: Fewer Rounds May Be Better Than More (2025.findings-emnlp)
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| Challenge: | Existing personality assessment datasets based on natural language do not consider interactivity. |
| Approach: | They propose to use a Chinese dataset to study the effects of different interaction rounds and agent personalities on personality assessment. |
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We Argue to Agree: Towards Personality-Driven Argumentation-Based Negotiation Dialogue Systems for Tourism (2025.findings-emnlp)
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| Challenge: | Argumentation mechanisms are integrated into negotiation dialogue systems to improve conflict resolution and adaptability. |
| Approach: | They propose a dataset of Argumentation Profile, Preference Profile, and Buying Style Profiles to generate personality-driven dialogues in negotiation dialogue systems. |
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Dynamic Personality in LLM Agents: A Framework for Evolutionary Modeling and Behavioral Analysis in the Prisoner’s Dilemma (2025.findings-acl)
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| Challenge: | Current models rely on static personality traits but lack natural selection processes and direct psychological metrics, failing to accurately capture authentic dynamic personality variations. |
| Approach: | They propose a framework that uses game payoffs as environmental feedback to drive adaptive personality evolution and analyze correlations between personality metrics and behavior. |
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Imperfectly Cooperative Human-AI Interactions: Comparing the Impacts of Human and AI Attributes in Simulated and User Studies (2026.findings-acl)
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Myke C. Cohen, Mingqian Zheng, Neel Bhandari, Hsien-Te Kao, Xuhui Zhou, Daniel Nguyen, Laura Cassani, Maarten Sap, Svitlana Volkova
| Challenge: | In simulations, personality traits and AI attributes were comparatively influential, but with actual human subjects, AI attributes – particularly transparency – were much more impactful. |
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| Outcome: | The results show that personality traits and AI attributes are comparatively influential in simulations, but with actual human subjects, they are much more impactful. |
Persona Dynamics: Unveiling the Impact of Persona Traits on Agents in Text-Based Games (2025.acl-long)
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| Challenge: | Text-based interactive environments have long presented formidable challenges for AI. |
| Approach: | They propose a method for projecting human personality traits onto agents to guide their behavior and integrate them into their policy-learning pipelines. |
| Outcome: | The proposed method induces personality in a text-based game agent by integrating personality profiles directly into the agent's policy-learning pipeline. |