# Human-Modeling in Sequential Decision-Making: An Analysis through the Lens of Human-Aware AI

arXiv: https://arxiv.org/abs/2405.07773 · HTML: https://mlsanity.com/paper/2405.07773/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 64 references that are in the map.
- First posted on arXiv 13 May 2024.

## Builds on (most cited first, 20 of 20)

- [Reward-rational (implicit) choice: A unifying formalism for reward learning](https://mlsanity.com/paper/2002.04833/) (2020)
- [Continuous Trajectory Generation Based on Two-Stage GAN](https://mlsanity.com/paper/2301.07103/) (2023)
- [PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception](https://mlsanity.com/paper/2103.01933/) (2021)
- [Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning](https://mlsanity.com/paper/2212.00904/) (2022)
- [Challenges of Human-Aware AI Systems](https://mlsanity.com/paper/1910.07089/) (2019)
- [On Exploiting Hitting Sets for Model Reconciliation](https://mlsanity.com/paper/2012.09274/) (2020)
- [Towards Collaborative Plan Acquisition through Theory of Mind Modeling in Situated Dialogue](https://mlsanity.com/paper/2305.11271/) (2023)
- [Expectation-Aware Planning: A Unifying Framework for Synthesizing and Executing Self-Explaining Plans for Human-Aware Planning](https://mlsanity.com/paper/1903.07269/) (2019)
- [Objective-aware Traffic Simulation via Inverse Reinforcement Learning](https://mlsanity.com/paper/2105.09560/) (2021)
- [Decentralized MCTS via Learned Teammate Models](https://mlsanity.com/paper/2003.08727/) (2020)
- [Solution Concepts in Hierarchical Games under Bounded Rationality with Applications to Autonomous Driving](https://mlsanity.com/paper/2009.10033/) (2020)
- [TANGO: Commonsense Generalization in Predicting Tool Interactions for Mobile Manipulators](https://mlsanity.com/paper/2105.04556/) (2021)
- [Multiple Plans are Better than One: Diverse Stochastic Planning](https://mlsanity.com/paper/2012.15485/) (2020)
- [A Unifying Bayesian Formulation of Measures of Interpretability in Human-AI](https://mlsanity.com/paper/2104.10743/) (2021)
- [Zero-Shot Assistance in Sequential Decision Problems](https://mlsanity.com/paper/2202.07364/) (2022)
- [A Mental-Model Centric Landscape of Human-AI Symbiosis](https://mlsanity.com/paper/2202.09447/) (2022)
- [Generalized dynamic cognitive hierarchy models for strategic driving behavior](https://mlsanity.com/paper/2109.09861/) (2021)
- [A Mental Model Based Framework of Trust](https://mlsanity.com/paper/2301.12569/) (2023)
- [In-game Residential Home Planning via Visual Context-aware Global Relation Learning](https://mlsanity.com/paper/2102.04035/) (2021)
- [Adaptation and Communication in Human-Robot Teaming to Handle Discrepancies in Agents' Beliefs about Plans](https://mlsanity.com/paper/2307.03362/) (2023)

## Abstract (arXiv)

"Human-aware" has become a popular keyword used to describe a particular class of AI systems that are designed to work and interact with humans. While there exists a surprising level of consistency among the works that use the label human-aware, the term itself mostly remains poorly understood. In this work, we retroactively try to provide an account of what constitutes a human-aware AI system. We see that human-aware AI is a design oriented paradigm, one that focuses on the need for modeling the humans it may interact with. Additionally, we see that this paradigm offers us intuitive dimensions to understand and categorize the kinds of interactions these systems might have with humans. We show the pedagogical value of these dimensions by using them as a tool to understand and review the current landscape of work related to human-AI systems that purport some form of human modeling. To fit the scope of a workshop paper, we specifically narrowed our review to papers that deal with sequential decision-making and were published in a major AI conference in the last three years. Our analysis helps identify the space of potential research problems that are currently being overlooked. We perform additional analysis on the degree to which these works make explicit reference to results from social science and whether they actually perform user-studies to validate their systems. We also provide an accounting of the various AI methods used by these works.