Epistemic Reasoning in Jason

This paper presents an extension to the Jason BDI language to allow qualitative reasoning under uncertainty. We demonstrate the need for such an extension using a challenge from the 2019 Multi-Agent Programming Contest (MAPC), namely localization for navigation. Given the ability to qualitatively reason about what the agent knows and what it considers possible (or impossible), these challenges become easier to express, reason about, and act upon in a Jason program. Through the use of epistemic logic and the epistemic reasoner in Hintikka's World, our extension allows agents to express epistemic queries; specifically, utilizing the class of single-agent S5 epistemic models to model-check queries about the agent's uncertainty. This paper also provides an evaluation of the overall performance and scalability of the extension's implementation to show how it impacts the agent's reasoning time; from the evaluation results, we use the official 2019 MAPC time constraints to examine the performance tradeoffs of using the presented extension to model and reason about uncertainty.