论文标题
建模人类行为第一部分 - 学习和信念方法
Modeling Human Behavior Part I -- Learning and Belief Approaches
论文作者
论文摘要
有明确的渴望对人类的行为进行建模和理解。研究涵盖该主题的研究趋势表明，明确的假设是，许多人将人类推理视为人工推理的预设标准。因此，诸如游戏理论，思想理论，机器学习等主题均集成了假定的人类推理组成部分的概念。这些是试图复制和理解人类行为的技术。此外，下一代自主和自适应系统将在很大程度上包括AI代理和人类作为团队合作的人。为了实现这一目标，自主代理人将需要能够嵌入人类行为的实用模型，这不仅使他们不仅复制人类模型作为一种“学习”的技术，而且可以理解用户的行为并预测其行为，以便与他们真正在共生中真正运作。本文的主要目的是对两个领域中最重要的方法进行简洁而系统的审查，以涉及人类行为的定量模型。具体而言，我们专注于（i）通过探索和反馈来学习行为模型或行为政策的技术，例如增强学习，以及（ii）直接模拟人类推理的机制，例如信念和偏见，而无需通过试验和错误进行学习。
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.
