A singular attribute of humankind is our ability to undertake novel,
cooperative behavior, or teamwork. This requires that we can communicate goals,
plans, and ideas between the brains of individuals to create shared
intentionality. Using the information processing model of David Marr, I derive
necessary characteristics of basic mechanisms to enable shared intentionality
between computational agents and indicate how these could be implemented in
present-day AI-based robots.
More speculatively, I suggest the mechanisms derived by this thought
experiment apply to humans and extend to provide explanations for human
rationality and aspects of intentional and phenomenal consciousness that accord
with observation. This yields what I call the Shared Intentionality First
Theory (SIFT) for rationality and consciousness.

本研究利用 David Marr 的信息处理模型，提出了计算机代理间实现共享意图的基本机制，并探讨了这些机制如何应用于现代基于人工智能的机器人中。另外，作者还通过推测性的思考实验，将这些机制扩展到了人类，提出了 “共享意图第一理论”（SIFT）用以解释人类的理性和意识现象。

共享意向性的计算机制，以及关于理性和意识的推测

On Computational Mechanisms for Shared Intentionality, and Speculation  on Rationality and Consciousness

Shared intentionality is a critical component in developing conscious AI
agents capable of collaboration, self-reflection, deliberation, and reasoning.
We formulate inference of shared intentionality as an inverse reinforcement
learning problem with logical reward specifications. We show how the approach
can infer task descriptions from demonstrations. We also extend our approach to
actively convey intentionality. We demonstrate the approach on a simple
grid-world example.

这篇研究论文探讨了共享意向性在开发具有合作、自我反思、思考和推理能力的有意识的 AI 代理中的关键作用，并将共享意向性的推断作为具有逻辑回报说明的逆强化学习问题来表述。研究表明了该方法如何从演示中推断出任务描述，并将其扩展到主动传达意向性，最后在一个简单的网格世界示例中展示了方法的有效性。