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null (Ed.)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.more » « less
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null (Ed.)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.more » « less
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There is an ever-growing literature on the power of prediction markets to harness “the wisdom of the crowd” from large groups of people. However, traditional prediction markets are not designed in a human-centered way, often restricting their own potential. This creates the opportunity to implement a cognitive science perspective on how to enhance the collective intelligence of the participants. Thus, we propose a new model for prediction markets that integrates human factors, cognitive science, game theory and machine learning to maximize collective intelligence. We do this by first identifying the connections between prediction markets and collective intelligence, to then use human factors techniques to analyze our design, culminating in the practical ways with which our design enables artificial intelligence to complement human intelligence.more » « less
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With multi-agent teams becoming more of a reality every day, it is important to create a common design model for multi-agent teams. These teams need to be able to function in dynamic environments and still communicate with any humans that may need a problem solved. Existing human-agent research can be used to purposefully create multi-agent teams that are interdependent but can still interact with humans. Rather than creating dynamic agents, the most effective way to overcome the dynamic nature of modern workloads is to create a dynamic team configuration, rather than individual member-agents that can change their roles. Multi-agent teams will require a variety of agents to be designed to cover a diverse subset of problems that need to be solved in the modern workforce. A model based on existing multi-agent teams that satisfies the needs of human-agent teams has been created to serve as a baseline for human-interactive multi-agent teams.more » « less
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In this paper we propose a new model for teamwork that integrates team cognition, collective intelligence, and artificial intelligence. We do this by first characterizing what sets team cognition and collectively intelligence apart, and then reviewing the literature on “superforecasting” and the ability for effectively coordinated teams to outperform predictions by large groups. Lastly, we delve into the ways in which teamwork can be enhanced by artificial intelligence through our model, finally highlighting the many areas of research worth exploring through interdisciplinary efforts.more » « less