Actions You Can Handle: Dependent Types for AI Plans
This addresses the verification challenge in AI planning for developers and researchers, offering a method to enhance reasoning capabilities beyond existing tools, though it is incremental in integrating type systems with planning.
The paper tackles the limitations of AI planners as programming languages by embedding their plans into the dependently-typed language Agda, enabling verification of more general and abstract plan properties and providing a holistic infrastructure for modeling plan execution.
Verification of AI is a challenge that has engineering, algorithmic and programming language components. For example, AI planners are deployed to model actions of autonomous agents. They comprise a number of searching algorithms that, given a set of specified properties, find a sequence of actions that satisfy these properties. Although AI planners are mature tools from the algorithmic and engineering points of view, they have limitations as programming languages. Decidable and efficient automated search entails restrictions on the syntax of the language, prohibiting use of higher-order properties or recursion. This paper proposes a methodology for embedding plans produced by AI planners into dependently-typed language Agda, which enables users to reason about and verify more general and abstract properties of plans, and also provides a more holistic programming language infrastructure for modelling plan execution.