Proficiency-Driven Decision-Making for Networks of Autonomous Agents

Autonomous agents play a crucial role in various modern fields, including emergency response and urban security. Their ability to operate effectively without direct human supervision is essential, especially in high-stakes situations. A key challenge is enabling these agents to evaluate their proficiency in completing tasks and use this evaluation for informed decision-making. This paper explores the use of a metric based on the assessment of autonomous agents’ proficiency and applies it to improve their decision-making at run time. In this context, proficiency self-assessment will improve agent navigation, enabling agents to more effectively complete their mission tasks, such as reaching a destination area and enhancing estimation accuracy.