One of the early goals of artificial intelligence (AI) was to create algorithms that exhibited behavior indistinguishable from human behavior (i.e., human-like behavior). Today, AI has diverged, often aiming to excel in tasks inspired by human capabilities and outperform humans, rather than replicating human cogntion and action. In this paper, I explore the overarching question of whether computational algorithms have achieved this initial goal of AI. I focus on dynamic decision-making, approaching the question from the perspective of computational cognitive science. I present a general cognitive algorithm that intends to emulate human decision-making in dynamic environments, as defined in instance-based learning theory (IBLT). I use the cognitive steps proposed in IBLT to organize and discuss current evidence that supports some of the human-likeness of the decision-making mechanisms. I also highlight the significant gaps in research that are required to improve current models and to create higher fidelity in computational algorithms to represent human decision processes. I conclude with concrete steps toward advancing the construction of algorithms that exhibit human-like behavior with the ultimate goal of supporting human dynamic decision-making.

中文翻译：

---

构建类人人工智能体：在动态环境中模拟人类决策的通用认知算法。


人工智能 (AI) 的早期目标之一是创建表现出与人类行为无法区分的算法（即类人行为）。如今，人工智能已经出现了分歧，通常旨在在受人类能力启发的任务中表现出色并超越人类，而不是复制人类的认知和行动。在本文中，我探讨了计算算法是否实现了人工智能的最初目标这一首要问题。我专注于动态决策，从计算认知科学的角度来处理这个问题。我提出了一种通用认知算法，旨在模拟动态环境中的人类决策，如基于实例的学习理论 (IBLT) 中所定义的。我使用 IBLT 中提出的认知步骤来组织和讨论当前的证据，这些证据支持决策机制的某些人类相似性。我还强调了改进当前模型和在计算算法中创建更高保真度以代表人类决策过程所需的研究中的重大差距。最后，我提出了推进算法构建的具体步骤，这些算法表现出类人行为，最终目标是支持人类动态决策。