Modeling Human Behavior Part I -- Learning and Belief Approaches

TL;DR

This paper reviews key approaches to modeling human behavior, focusing on learning-based methods like reinforcement learning and reasoning-based models involving beliefs and biases, to enable AI-human collaboration.

Contribution

It provides a systematic review of methods for modeling human behavior through learning and reasoning, highlighting their roles in autonomous systems and AI-human interaction.

Findings

Reinforcement learning models adapt to human feedback.

Models of beliefs and biases capture human reasoning mechanisms.

Approaches facilitate AI understanding and prediction of human actions.

Abstract

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.