Introducing k-lingo: a k-depth Bounded Version of ASP System Clingo

Depth-Bounded Boolean Logics (DBBL for short) are well-understood frameworks to model rational agents equipped with limited deductive capabilities. These Logics use a parameter k>=0 to limit the amount of virtual information, i.e., the information that the agent may temporarily assume throughout the deductive process. This restriction brings several advantageous properties over classical Propositional Logic, including polynomial decision procedures for deducibility and refutability. Inspired by DBBL, we propose a limited-depth version of the popular ASP system clingo, tentatively dubbed k-lingo after the bound k on virtual information. We illustrate the connection between DBBL and ASP through examples involving both proof-theoretical and implementative aspects. The paper concludes with some comments on future work, which include a computational complexity characterization of the system, applications to multi-agent systems and feasible approximations of probability functions.