论文标题
容易出错的AI和人类代理商之间委派的认知框架
A Cognitive Framework for Delegation Between Error-Prone AI and Human Agents
论文作者
论文摘要
随着人类以越来越多的速度与基于AI的系统相互作用，有必要确保人造系统以反映对人类理解的方式起作用。对于人类和在同一环境中运行的人造AI代理，我们从代理人的角度观察了对人类的行为或能力的理解和响应的重要性，以及将决策委派给人类或代理商的可能性，具体取决于某个时间的某个时间，具体视谁被认为更合适。这样的功能将确保整个人类系统的响应能力和效用。为此，我们研究了认知灵感的行为模型的使用来预测人类和AI代理的行为。通过使用中介实体，使用预测的行为以及相关的相关性能，用于通过使用中介实体来委托人与AI代理之间的控制。正如我们所证明的那样，这允许在追求目标中克服人类或代理人的潜在缺点。
With humans interacting with AI-based systems at an increasing rate, it is necessary to ensure the artificial systems are acting in a manner which reflects understanding of the human. In the case of humans and artificial AI agents operating in the same environment, we note the significance of comprehension and response to the actions or capabilities of a human from an agent's perspective, as well as the possibility to delegate decisions either to humans or to agents, depending on who is deemed more suitable at a certain point in time. Such capabilities will ensure an improved responsiveness and utility of the entire human-AI system. To that end, we investigate the use of cognitively inspired models of behavior to predict the behavior of both human and AI agents. The predicted behavior, and associated performance with respect to a certain goal, is used to delegate control between humans and AI agents through the use of an intermediary entity. As we demonstrate, this allows overcoming potential shortcomings of either humans or agents in the pursuit of a goal.
