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.

翻译：随着人类与基于人工智能的系统交互日益频繁，确保人工系统能够以理解人类行为的方式运作至关重要。在人类与AI智能体共存的场景中，我们注意到从智能体视角理解人类行为或能力并对其作出回应的意义，以及根据特定时间节点谁更合适将决策委托给人类或智能体的可能性。这种能力将提升整个人机系统的响应效率与实用性。为此，我们探索采用认知启发的行为模型来预测人类与AI智能体的行为。通过中间实体，我们利用预测行为及其与特定目标相关的性能表现，在人类与AI智能体之间进行控制权委托。研究表明，这能有效克服人类或智能体在实现目标过程中可能存在的局限性。