Effective Maintenance of Computational Theory of Mind for Human-AI Collaboration

Abstract

In order to enhance collaboration between humans and artificially intelligent agents, it is crucial to equip the computational agents with capabilities commonly used by humans. One of these capabilities is called Theory of Mind (ToM) reasoning, which is the human ability to reason about the mental contents of others, such as their beliefs, desires, and goals. For an agent to efficiently benefit from having a functioning computational ToM of its human partner in a collaboration, it needs to be practical in computationally tracking their mental attitudes and it needs to create approximate ToM models that can be effectively maintained. In this paper, we propose a computational ToM mechanism based on abstracting beliefs and knowledge into higher-level human concepts, referred to as abstractions. These abstractions, similar to those guiding human interactions (e.g., trust), form the basis of our modular agent architecture. We address an important challenge related to maintaining abstractions effectively, namely abstraction consistency. We propose different approaches to study this challenge in the context of a scenario inspired by a medical domain and provide an experimental evaluation over agent simulations.

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