An agent-based persuasion model using emotion-driven concession and multi-objective optimization

IF 2 3区计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS

Autonomous Agents and Multi-Agent Systems Pub Date : 2024-07-09 DOI:10.1007/s10458-024-09664-7

Zhenwu Wang, Jiayin Shen, Xiaosong Tang, Mengjie Han, Zhenhua Feng, Jinghua Wu

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引用次数: 0

Abstract

Multi-attribute negotiation is essentially a multi-objective optimization (MOO) problem, where models of agent-based emotional persuasion (EP) can exhibit characteristics of anthropomorphism. This paper proposes a novel EP model by fusing the strategy of emotion-driven concession with the method of multi-objective optimization (EDC-MOO). Firstly, a comprehensive emotion model is designed to enhance the authenticity of the emotion. A novel concession strategy is then proposed to enable the concession to be dynamically tuned by the emotions of the agents. Finally, a new EP model is constructed by integrating emotion, historical transaction, persuasion behavior, and concession strategy under the framework of MOO. Comprehensive experiments on bilateral negotiation are conducted to illustrate and validate the effectiveness of EDC-MOO. These include an analysis of negotiations under five distinct persuasion styles, a comparison of EDC-MOO with a non-emotion-based MOO negotiation model and classic trade-off strategies, negotiations between emotion-driven and non-emotion-driven agents, and negotiations involving human participants. A detailed analysis of parameter sensitivity is also discussed. Experimental results show that the proposed EDC-MOO model can enhance the diversity of the negotiation process and the anthropomorphism of the bilateral agents, thereby improving the social welfare of both parties.

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使用情感驱动让步和多目标优化的基于代理的说服模型

多属性谈判本质上是一个多目标优化（MOO）问题，基于代理的情感说服（EP）模型可能表现出拟人化的特征。本文通过融合情感驱动让步策略和多目标优化方法（EDC-MOO），提出了一种新颖的情感说服（EP）模型。首先，设计了一个全面的情感模型，以增强情感的真实性。然后，提出了一种新颖的让步策略，使让步能够根据代理人的情绪进行动态调整。最后，在 MOO 框架下，通过整合情感、历史交易、说服行为和让步策略，构建了一个新的 EP 模型。为了说明和验证 EDC-MOO 的有效性，我们对双边谈判进行了综合实验。这些实验包括分析五种不同说服风格下的谈判、EDC-MOO 与非基于情感的 MOO 谈判模型和经典权衡策略的比较、情感驱动和非情感驱动代理之间的谈判，以及涉及人类参与者的谈判。此外，还讨论了对参数敏感性的详细分析。实验结果表明，所提出的 EDC-MOO 模型可以增强谈判过程的多样性和双边代理的拟人化，从而提高双方的社会福利。

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来源期刊

Autonomous Agents and Multi-Agent Systems 工程技术-计算机：人工智能

CiteScore

6.00

自引率

5.30%

发文量

审稿时长

>12 weeks

期刊介绍： This is the official journal of the International Foundation for Autonomous Agents and Multi-Agent Systems. It provides a leading forum for disseminating significant original research results in the foundations, theory, development, analysis, and applications of autonomous agents and multi-agent systems. Coverage in Autonomous Agents and Multi-Agent Systems includes, but is not limited to: Agent decision-making architectures and their evaluation, including: cognitive models; knowledge representation; logics for agency; ontological reasoning; planning (single and multi-agent); reasoning (single and multi-agent) Cooperation and teamwork, including: distributed problem solving; human-robot/agent interaction; multi-user/multi-virtual-agent interaction; coalition formation; coordination Agent communication languages, including: their semantics, pragmatics, and implementation; agent communication protocols and conversations; agent commitments; speech act theory Ontologies for agent systems, agents and the semantic web, agents and semantic web services, Grid-based systems, and service-oriented computing Agent societies and societal issues, including: artificial social systems; environments, organizations and institutions; ethical and legal issues; privacy, safety and security; trust, reliability and reputation Agent-based system development, including: agent development techniques, tools and environments; agent programming languages; agent specification or validation languages Agent-based simulation, including: emergent behavior; participatory simulation; simulation techniques, tools and environments; social simulation Agreement technologies, including: argumentation; collective decision making; judgment aggregation and belief merging; negotiation; norms Economic paradigms, including: auction and mechanism design; bargaining and negotiation; economically-motivated agents; game theory (cooperative and non-cooperative); social choice and voting Learning agents, including: computational architectures for learning agents; evolution, adaptation; multi-agent learning. Robotic agents, including: integrated perception, cognition, and action; cognitive robotics; robot planning (including action and motion planning); multi-robot systems. Virtual agents, including: agents in games and virtual environments; companion and coaching agents; modeling personality, emotions; multimodal interaction; verbal and non-verbal expressiveness Significant, novel applications of agent technology Comprehensive reviews and authoritative tutorials of research and practice in agent systems Comprehensive and authoritative reviews of books dealing with agents and multi-agent systems.