基于信任案例的agent协作模型

Q1 Computer Science

Foundations and Trends in Human-Computer Interaction Pub Date : 2019-01-01 DOI:10.4018/978-1-5225-2255-3.CH416

Felipe Boff, Fabiana Lorenzi

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引用次数: 1

摘要

本章展示了一种使用基于案例的推理(CBR)来计算代理协作中的信任级别的方法。提出的发展设想在一般信任等级的更新过程中考虑agent之间的所有交互。这些协作也存储在CBR数据库中。每个新的交互都会基于类似的情况计算情景信任级别。此信任级别将与全局信任级别进行权衡，根据请求的协作创建一个指示器，而不排除协作历史。

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A Trust Case-Based Model Applied to Agents Collaboration

This chapter shows an approach for developing a method that uses case-based reasoning (CBR) for calculating trust levels in agents' collaboration. The proposed development foresees all interactions between agents are considered in the updating process of general trust level. These collaborations are also stored in a CBR database. Each new interaction calculates a situational trust level based on similar cases. This trust level will be weighed against the global trust level, creating an indicator based on the requested collaboration without excluding the collaboration history.

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来源期刊

Foundations and Trends in Human-Computer Interaction Computer Science-Computer Science Applications

CiteScore

10.10

自引率

0.00%

发文量

期刊介绍： Foundations and Trends® in Human-Computer Interaction publishes surveys and tutorials in the following topics: - History of the research community - Design and Evaluation - Theory - Technology - Computer Supported Cooperative Work - Interdisciplinary influence - Advanced topics and trends - Information visualization