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This work demonstrates that a novice user of the Monterey Phoenix (MP) tool can encode their expertise of robot mission tasks in the form of a finite state machine. State machine diagrams are among several approaches available for describing behavior. They are appropriate for capturing and understanding behavior rules of systems that can be described in terms of possible states and state transitions. Tools that offer state machine automation enable modelers to test the activation of states and trace the transitions between states, providing assistance with verification and validation of the behavior logic. The United States Military Academy (USMA) at West Point’s Robotics Research Center (RRC) in collaboration with the Naval Postgraduate School (NPS), developed a minimal executable state machine model to test its implementation of behavior logic for a single robot agent in Project Aquaticus, a human-robot teaming capture the flag competition. Monterey Phoenix, a formal behavior modeling language, approach, and tool was used to express the behavior logic of the robots participating in this game as a finite state machine model. MP was used to generate every possible trace through the state machine behavior logic up to the specified scope limit. This model informed the team’s understanding of the desired states for the single robot competitor in Aquaticus, assisted in identifying behavior improvements, and identifying dependencies among behaviors and where the process could possibly go wrong. In essence, this work demonstrates that encoding the finite state machine of the single robot tasks in MP allows experts to verify and validate expected robot behaviors and adjust as needed. Future work will look to expand from the single agent modeling to multi-agent modeling in MP to generate and verify possible sequences of events for the Aquaticus competition between competing teams. By using this modeling method to understand the state of each actor and their response to inputs from other actors, the team expects to improve the communication within the human-robot team and ultimately achieve a better understanding of command intent in complex, contested environments. Today’s mission-critical systems are actually, “systems of systems” with complexity that will surpass the designer’s cognitive ability to anticipate all interactions. Without tools, languages, and methodologies to analyze system-level behavior early in the design, mission systems can be at risk of emergent behaviors that may negatively impact safety and security. Our team believes that the MP environment can assist commanders in anticipating potentially unsafe or insecure emergent behaviors of human-robot teams in complex, contested environments and adjust human intent/command intent accordingly for allowable combat crew drill behaviors required to achieve assigned tasks and missions.
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