# A Mental-Model Centric Landscape of Human-AI Symbiosis

arXiv: https://arxiv.org/abs/2202.09447 · HTML: https://mlsanity.com/paper/2202.09447/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 65 references that are in the map.
- First posted on arXiv 18 Feb 2022.

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

- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [Learning to Complement Humans](https://mlsanity.com/paper/2005.00582/) (2020)
- [Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure](https://mlsanity.com/paper/1809.07424/) (2018)
- [Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations](https://mlsanity.com/paper/2002.01080/) (2020)
- [Explicability? Legibility? Predictability? Transparency? Privacy? Security? The Emerging Landscape of Interpretable Agent Behavior](https://mlsanity.com/paper/1811.09722/) (2018)
- [Plan Explicability and Predictability for Robot Task Planning](https://mlsanity.com/paper/1511.08158/) (2015)
- [Investigating Human + Machine Complementarity for Recidivism Predictions](https://mlsanity.com/paper/1808.09123/) (2018)
- [On Human Intellect and Machine Failures: Troubleshooting Integrative Machine Learning Systems](https://mlsanity.com/paper/1611.08309/) (2016)
- [A Unified Framework for Planning in Adversarial and Cooperative Environments](https://mlsanity.com/paper/1802.06137/) (2018)
- [Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks](https://mlsanity.com/paper/2007.12803/) (2020)
- [Expressing Robot Incapability](https://mlsanity.com/paper/1810.08167/) (2018)
- [Predicting Human Behavior in Unrepeated, Simultaneous-Move Games](https://mlsanity.com/paper/1306.0918/) (2013)
- [Signaling Friends and Head-Faking Enemies Simultaneously: Balancing Goal Obfuscation and Goal Legibility](https://mlsanity.com/paper/1905.10672/) (2019)
- [Model-Free Model Reconciliation](https://mlsanity.com/paper/1903.07198/) (2019)
- [Expectation-Aware Planning: A Unifying Framework for Synthesizing and Executing Self-Explaining Plans for Human-Aware Planning](https://mlsanity.com/paper/1903.07269/) (2019)
- [Not all users are the same: Providing personalized explanations for sequential decision making problems](https://mlsanity.com/paper/2106.12207/) (2021)
- [A Unifying Bayesian Formulation of Measures of Interpretability in Human-AI](https://mlsanity.com/paper/2104.10743/) (2021)
- [Nonverbal Robot Feedback for Human Teachers](https://mlsanity.com/paper/1911.02320/) (2019)
- [Model Elicitation through Direct Questioning](https://mlsanity.com/paper/2011.12262/) (2020)
- [Minimizing Robot Navigation-Graph For Position-Based Predictability By Humans](https://mlsanity.com/paper/2010.15255/) (2020)

## Built on by (most cited first, 4 of 4)

- [Theory of Mind abilities of Large Language Models in Human-Robot Interaction : An Illusion?](https://mlsanity.com/paper/2401.05302/) (2024)
- [Adaptation and Communication in Human-Robot Teaming to Handle Discrepancies in Agents' Beliefs about Plans](https://mlsanity.com/paper/2307.03362/) (2023)
- [Human-Modeling in Sequential Decision-Making: An Analysis through the Lens of Human-Aware AI](https://mlsanity.com/paper/2405.07773/) (2024)
- [AI Models Can Predict and Collaboratively Modulate Human Memory Search](https://mlsanity.com/paper/2608.26152/) (2026)

## Abstract (arXiv)

There has been significant recent interest in developing AI agents capable of effectively interacting and teaming with humans. While each of these works try to tackle a problem quite central to the problem of human-AI interaction, they tend to rely on myopic formulations that obscure the possible inter-relatedness and complementarity of many of these works. The human-aware AI framework was a recent effort to provide a unified account for human-AI interaction by casting them in terms of their relationship to various mental models. Unfortunately, the current accounts of human-aware AI are insufficient to explain the landscape of the work doing in the space of human-AI interaction due to their focus on limited settings. In this paper, we aim to correct this shortcoming by introducing a significantly general version of human-aware AI interaction scheme, called generalized human-aware interaction (GHAI), that talks about (mental) models of six types. Through this paper, we will see how this new framework allows us to capture the various works done in the space of human-AI interaction and identify the fundamental behavioral patterns supported by these works. We will also use this framework to identify potential gaps in the current literature and suggest future research directions to address these shortcomings.