There is a clear desire to model and comprehend human behavior. Trends in research covering this topic show a clear assumption that many view human reasoning as the presupposed standard in artificial reasoning. As such, topics such as game theory, theory of mind, machine learning, etc. all integrate concepts which are assumed components of human reasoning. These serve as techniques to attempt to both replicate and understand the behaviors of humans. In addition, next generation autonomous and adaptive systems will largely include AI agents and humans working together as teams. To make this possible, autonomous agents will require the ability to embed practical models of human behavior, which allow them not only to replicate human models as a technique to "learn", but to to understand the actions of users and anticipate their behavior, so as to truly operate in symbiosis with them. The main objective of this paper it to provide a succinct yet systematic review of the most important approaches in two areas dealing with quantitative models of human behaviors. Specifically, we focus on (i) techniques which learn a model or policy of behavior through exploration and feedback, such as Reinforcement Learning, and (ii) directly model mechanisms of human reasoning, such as beliefs and bias, without going necessarily learning via trial-and-error.
翻译:有关人类行为的研究趋势表明,许多人将人类推理视为人为推理的先天标准。因此,游戏理论、思想理论、机器学习等课题都综合了人类推理中假定组成部分的概念。这些是试图复制和理解人类行为的方法。此外,下一代自主和适应性系统将主要包括大赦国际的代理人和团队合作的人类。为了使这一点成为可能,自主代理人将要求有能力嵌入实际的人类行为模型,不仅允许他们复制人类模型作为“阅读”技术,而且允许他们理解用户的行动和预测其行为,以便真正地在与它们和谐相处中运作。本文的主要目的是对涉及人类行为定量模型的两个领域中最重要的方法进行简洁而系统的审查。具体地说,我们侧重于(一)通过探索和反馈学习行为模型或政策的技术,例如加强学习,以及(二)直接的人类推理模型机制,例如信念和偏见,必须不通过试验学习。