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Technological advancement goes hand in hand with economic advancement, meaning applied industries like manufacturing, medicine, and retail are set to leverage new practices like human-autonomy teams. These human-autonomy teams call for deep integration between artificial intelligence and the human workers that make up a majority of the workforce. This paper identifies the core principles of the human-autonomy teaming literature relevant to the integration of human-autonomy teams in applied contexts and research due to this large scale implementation of human-autonomy teams. A framework is built and defined from these fundamental concepts, with specific examples of its use in applied contexts and the interactions between various components of the framework. This framework can be utilized by practitioners of human-autonomy teams, allowing them to make informed decisions regarding the integration and training of human-autonomy teams. 

Advances in artificial intelligence are constantly increasing its validity as a team member enabling it to effectively work alongside humans and other artificial teammates. Unfortunately, the digital nature of artificial teammates and their restrictive communication and coordination requirements complicate the interaction patterns that exist. In light of this challenge, we create a theoretical framework that details the possible interactions in human-agent teams, emphasizing interactions through groupware, which is based on literature regarding groupware and human-agent teamwork. As artificial intelligence changes and advances, the interaction in human agent teams will also advance, meaning interaction frameworks and groupware must adapt to these changes. We provide examples and a discussion of the frameworks ability to adapt based on advancements in relevant research areas like natural language processing and artificial general intelligence. The results are a framework that detail human-agent interaction throughout the coming years, which can be used to guide groupware development. 

This paper creates and defines a framework for building and implementing human-autonomy teaming experiments that enable the utilization of modern reinforcement learning models. These models are used to train artificial agents to then interact alongside humans in a human-autonomy team. The framework was synthesized from experience gained redesigning a previously known and validated team task simulation environment known as NeoCITIES. Through this redesign, several important high-level distinctions were made that regarded both the artificial agent and the task simulation itself. The distinctions within the framework include gamification, access to high-performance computing, a proper reward function, an appropriate team task simulation, and customizability. This framework enables researchers to create experiments that are more usable for the human and more closely resemble real-world human-autonomy interactions. The framework also allows researchers to create veritable and robust experimental platforms meant to study human-autonomy teaming for years to come.