A Mental-Model Centric Landscape of Human-AI Symbiosis
Zahra Zahedi, Sarath Sreedharan, Subbarao Kambhampati
School Computing and AI, Arizona State UniversityIntroduction➢ There is a lack of a unifying framework that can make sense of the diverse challenges andtools used in the field of Human AI interaction.
➢ This paper presents a new framework called GHAI (Generalized Human-Awareinteraction) that addresses this gap.
➢ GHAI not only allows for scenarios where the human may be an active participant but also
introduces the notion of a true task model that captures the true joint task specificationof both the human and AI agent.
➢ Our model separates the agent's belief about the true underlying joint task, from theother agent's perception of it.
➢ As discussed in the paper, this framework is successful in unifying works from several
communities to address different modes of human-AI interaction including human-aware
planning, human-AI cohabitation, human-robot interaction, human-in-the-loop ML, andhuman-AI symbiosis.

Landscape of Human-AI Symbiosis➢ Model Information from AI Agent’s End (𝑴∗𝒓 ) -- works that mostly use
the AI models to update models held by the human.
We can further categorize works based on➢ the human models that will be updated (i.e., 𝑀h𝑅 or 𝑀𝐻 )➢ Whether or not the AI agent makes use of a mental model of thehuman (𝑀𝑟𝐻 ) to generate the required information

Generalized Human-Aware Interaction➢∗
Model Information from Human’s End (𝑴𝒉 ) -- works that use the Humanmental models to update AI agent models.
We have model information coming from 𝑀ℎ∗ that is used to➢ update 𝑀𝑅 , 𝑀𝑟𝐻 , or both
➢ The human might use 𝑀ℎ𝑅 to select this information
Model Information from Human’s End (M∗h)

Agents– Our central modeling captures the interaction between a human (H) and AI agent(R)
Model– A task model (𝑀.. ) is any mathematical model that encodes among other things, anentities beliefs about task objectives, state of the world and how the world may evolve on its
own or in response to an agent action. (e.g. MDPs, differential system equations or symbolicmodels like PDDL)Task models 𝑴∗. – This model captures the entirety of the task. It consists of all of theactions, objectives, preferences of both agents and additional facts about the world statethat may not fall into the purview of the individual agent models. We have the ground truth
model 𝑀∗ (𝑀∗∗ ), and the task models maintained by each of the∗agents (𝑀ℎ and 𝑀𝑟∗ ).
Model of each agent 𝑴𝑹 and 𝑴𝑯 – This is the model
each agent ascribes to themselves. This determines what actions each agent believes they
could perform and the objectives and preferences they are trying to satisfy. These models
are also part of each agent’s beliefs about what the true task model is, i.e., 𝑀𝑅 is part of𝑀𝑟∗ and 𝑀𝐻 is part of 𝑀ℎ∗ .
𝑯One agent’s model of the other 𝑴𝑹and𝑴𝒓 – These consist of what one agent believes
𝒉the model of the other agent is, i.e, 𝑀ℎ𝑅 is the human’s belief about 𝑀𝑅 , and 𝑀𝑟𝐻 is the AI
agent’s belief about 𝑀𝐻 .
Decisions – We will generally denote decisions as 𝜋. with thesubscript corresponding to the model from which it is derived.
This is not limited to a specific decision type and depending on the exact problem the spaceof decisions could vary from single shot labels one may associate with a classification task toa policy to be carried out by an embodied agent.

➢ Model Following Behavior -- how agents may choose decisions that take into
account multiple models.
In particular, we have
➢ Multi model alignmento 𝑀𝑟∗ and 𝑀ℎ𝑅o 𝑀𝑟∗ and 𝑀𝑟𝐻o 𝑀𝑟∗ and 𝑀ℎ∗o 𝑀ℎ∗ and 𝑀ℎ𝑅➢ Response seeking behavior

Acknowledgements
This research is supported in part by ONR grants N00014-16-1-2892, N0001418-1- 2442, N00014-18-1-2840, N00014-9-1-2119, AFOSR grant FA9550-18-10067, DARPA SAIL-ON grant W911NF-19- 2-0006, and a JP Morgan AI Faculty
Research grant.

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