在交互动态影响图中改善未知行为决策
Yinghui Pan1, Mengen Zhou1, Biyang Ma2
1School of Artificial Intelligence, Shenzhen University, Shenzhen, China.
概括
这项研究将群集智能 (SI) 引入交互动态影响图 (I-DID),以建模未知的代理行为. 这有助于为复杂的多代理环境制定决策框架.
科学领域:
- 人工智能
- 决策科学
- 多个代理系统
背景情况:
- 交互动态影响图 (I-DID) 为在部分可观测环境中交互的代理提供了一个决策框架.
- I-DID的一个关键挑战是模拟其他代理的未知或适应性行为,这是传统方法无法处理的.
- 当面对不可预测的对手或合作者时,这种限制阻碍了主体代理人优化策略的能力.
研究的目的:
- 通过集群智能 (SI) 技术来解决IDID中未知代理行为的挑战.
- 在I-DID框架内开发创新方法来产生多样化和适应性的代理行为.
- 分析基于SI的行为生成对受试者的决策质量的影响.
主要方法:
- 适应两个不同的群集智能 (SI) 算法来生成IDID中的代理行为.
- 理论分析SI算法对受试者的决策质量的影响.
- 在两个标准问题领域对拟议的基于SI的方法的实证评估.
主要成果:
- 群体智能技术有效地产生了一组能够代表各种代理类型的行为.
- 理论分析表明SI驱动的行为对受试者的决策质量产生了积极影响.
- 经验结果证明了在常见问题设置中提出的方法的实际性能和实用性.
结论:
- 通过模拟复杂的代理互动来增强交互动态影响图.
- SI的整合解决了长期存在的未知代理行为问题,改善了决策能力.
- 这项研究为开发更具适应性和智能的多代理系统提供了坚实的基础.
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