区间粗糙流变学和描述逻辑:语义Web本体中不精确的形式化处理方法

P. Klinov, Julia M. Taylor, L. Mazlack
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引用次数: 9

摘要

本文探讨了基于代理之间信任和自治模型的委托决策。在社会丰富的环境中,人工智能体的信任和自主性是理性委托决策的关键属性。社会代理人受到许多社会属性的影响,如仁慈、社会交换、权力和规范。我们提出了基于这些社会属性的代理之间委托的信任和自治的认知启发工作模型。这些模型在一组代理的模拟中得到了验证,这些代理为了找到最适合社会的个体来执行任务而委托任务。我们以这样一种客观的方式定义自主性,即对于一项任务具有最高自主性的代理通常是最适合社会的。仁慈、社会回报和规范最直接地促进了自主性,并与社会适应性成比例。衍生的社会权力直接影响社会适宜性,而独立分配的社会权力等级则凌驾于社会适宜性之上。我们表明,在不同的社会环境下,代理群体中的信任和自主性会发生变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Interval rough mereology and description logic: An approach to formal treatment of imprecision in the Semantic Web ontologies
This paper explores delegation decisions predicated on models of trust and autonomy among agents. In socially rich environments, trust and autonomy of artificial agents are key attributes for rational delegation decisions. Social agents are affected by many social attributes such as benevolence, social exchanges, power, and norms. We present cognitively inspired working models of trust and autonomy for delegation among agents that accounts for these social attributes. These models are validated in a simulation of a group of agents who delegate tasks in order to locate the most socially well-suited individuals for performing that task. We define autonomy in such an objective manner that agents who have the highest autonomy with respect to a task will generally be the most socially well suited. Benevolence, social reciprocation, and norms most directly contribute to autonomy and proportionally to social suitability. Derived social power directly contributes to social suitability, whereas independently assigned social power ranking overrides the suitability. We show that trust and autonomy in an agent group changes under various social settings.
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