With advances in machine learning, autonomous agents are increasingly able to navigate uncertain operational environments, as is the case within the multi-domain operations (MDO) paradigm. When teaming with humans, autonomous agents may flexibly switch between passive bystander and active executor depending on the task requirements and the actions being taken by partners (whether human or agent). In many tasks, it is possible that a well-trained agent's performance will exceed that of a human, in part because the agent's performance is less likely to degrade over time (e.g., due to fatigue). This potential difference in performance might lead to complacency, which is a state defined by over-trust in automated systems. This paper investigates the effects of complacency in human-agent teams, where agents and humans have the same capabilities in a simulated version of the predator-prey pursuit task. We compare subjective measures of the human's predisposition to complacency and trust using various scales, and we validate their beliefs by quantifying complacency through various metrics associated with the actions taken during the task with trained agents of varying reliability levels. By evaluating the effect of complacency on performance, we can attribute a degree of variation in human performance in this task to complacency. We can then account for an individual human's complacency measure to customize their agent teammates and human-in-the-loop requirements (either to minimize or compensate for the human's complacency) to optimize team performance.
When considering collaboration among agents in multi-agent systems, individual and team measures of performance are used to describe the collaboration. Typically, the definition of collaboration is limited in that it is only indicative of coordination required for a small class of tasks wherein this coordination is necessary for task completion (e.g. two or more agents needed to lift a heavy object). In this work, we aim to present a method that may be used to classify individual and group behaviors, enabling the measurement of collaboration among agents. We demonstrate the capability to use performance and behavioral data from computational learning agents in a predator-prey pursuit task to produce ergodic spatial distributions. Ergodicity is shown quantitatively and used to benchmark performance. The ergodic distributions shown, reflect the learned policies developed through multi-agent reinforcement learning (MARL). We also demonstrate that independently trained models produce distinctly different behavior, as revealed through ergodic spatial distributions. The ergodicity of the agents’ behavior provides both a potential path for classifying group behavior, predicting performance of group behavior with novel partners, and a quantifiable measure of collaboration built from explicitly aligned goals (i.e., cooperation) as a result of behavioral interdependencies.
Artificial intelligence (AI) has enormous potential for military applications. Fully realizing the conceived benefits of AI requires effective interactions among Soldiers and computational agents in highly uncertain and unconstrained operational environments. Because AI can be complex and unpredictable, computational agents should support their human teammates by adapting their behavior to the human’s elected strategy for a given task, facilitating mutuallyadaptive behavior within the team. While some situations entail explicit and easy-to-understand human top-down strategies, more often than not, human strategies tend to be implicit, ad hoc, exploratory, and difficult to describe. In order to facilitate mutually-adaptive human-agent team behavior, computational teammates must identify, adapt, and modify their behaviors to support human strategies with little or no a priori experience. This challenge may be achieved by training learning agents with examples of successful group strategies. Therefore, this paper focuses on an algorithmic approach to extract group strategies from multi-agent teaming behaviors in a game-theoretic environment: predator-prey pursuit. Group strategies are illuminated with a new method inspired from Graph Theory. This method treats agents as vertices to generate a timeseries of group dynamics and analytically compares timeseries segments to identify group coordinated behaviors. Ultimately, this approach may lead to the design of agents that can recognize and fall in line with strategies implicitly adopted by human teammates. This work can provide a substantial advance to the field of humanagent teaming by facilitating natural interactions within heterogeneous teams.
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