Prudens: An Argumentation-Based Language for Cognitive Assistants

Vassilis Markos, Loizos Michael

Abstract: In this short system paper, we present our implementation of a prioritized rule-based language for representing actionable policies, in the context of developing cognitive assistants. The language is associated with a provably efficient deduction process, and owing it to its interpretation under an argumentative semantics it can naturally offer ante-hoc explanations on its drawn inferences. Relatedly, the language is associated with a knowledge acquisition process based on the paradigm of machine coaching, guaranteeing the probable approximate correctness of the acquired knowledge against a target policy. The paper focuses on demonstrating the implemented features of the representation language and its exposed APIs and libraries, and discusses some of its more advanced features that allow the calling of procedural code, and the computation of in-line operations when evaluating rules.