Generative Datalog and Answer Set Programming - Extended Abstract

Generative Datalog is an extension of Datalog that incorporates constructs for referencing parameterized probability distributions. This augmentation transforms the evaluation of a Generative Datalog program into a stochastic process, resulting in a declarative formalism suitable for modeling and analyzing other stochastic processes. This work provides an introduction to Generative Datalog through the lens of Answer Set Programming (ASP), demonstrating how Generative Datalog can explain the output of ASP systems that include @-terms referencing probability distributions. From a theoretical point of view, extending the semantics of Generative Datalog to stable negation proved to be challenging due to the richness of ASP relative to Datalog in terms of linguistic constructs. On a more pragmatic side, the connection between the two formalisms lays the foundation for implementing Generative Datalog atop efficient ASP systems, making it a practical solution for real-world applications.