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Artificial agents (both embodied robots and software agents) that interact with humans are increasing at an exceptional rate. Yet, achieving seamless collaboration between artificial agents and humans in the real world remains an active problem [Thomaz et al., 2016]. A key challenge is that the agents need to make decisions without complete information about their shared environment and collaborators. For instance, a human-robot team performing a rescue operation after a disaster may not have an accurate map of their surroundings. Even in structured domains, such as manufacturing, a robot might not know the goals or preferences of its human collaborators [Unhelkar et al., 2018]. Algorithmically, this challenge manifests itself as a problem of decision-making under uncertainty in which the agent has to reason about the latent states of its environment and human collaborator. However, in practice, quantifying this uncertainty (i.e., the state transition function) and even specifying the features (i.e., the relevant states) of human-machine collaboration is difficult. Thus, the objective of this thesis research is to develop novel algorithms that enable artificial agents to learn and reason about the latent states of humanmachine collaboration and achieve fluent interaction.

Learning and Communicating the Latent States of Human-Machine Collaboration