Cplus2ASP: Computing Action Language C+ in Answer Set Programming

📅 2026-05-10
📈 Citations: 0
Influential: 0
📄 PDF
🤖 AI Summary
This work proposes an efficient answer set programming (ASP)-based translation and reasoning framework for the deterministic fragment of the action language C+. By integrating multiple theoretical advances into a composite translation mechanism, it achieves incremental grounding of C+ specifications within iclingo for the first time and extends the module theorem to guarantee semantic correctness of nested expressions. The resulting toolchain—combining f2lp, clingo, iclingo, and as2transition—supports external Lua atoms and multimodal translation strategies. The system substantially improves solving efficiency, maintains full compatibility with the input format of Causal Calculator v2, and naturally generalizes to other action languages such as B and BC.
📝 Abstract
We present Version 2 of system Cplus2ASP, which implements the definite fragment of action language C+. Its input language is fully compatible with the language of the Causal Calculator Version 2, but the new system is significantly faster thanks to modern answer set solving techniques. The translation implemented in the system is a composition of several recent theoretical results. The system orchestrates a tool chain, consisting of f2lp, clingo, iclingo, and as2transition. Under the incremental execution mode, the system translates a C+ description into the input language of iclingo, exploiting its incremental grounding mechanism. The correctness of this execution is justified by the module theorem extended to programs with nested expressions. In addition, the input language of the system has many useful features, such as external atoms by means of Lua calls and the user interactive mode. The system supports extensible multi-modal translations for other action languages, such as B and BC, as well.
Problem

Research questions and friction points this paper is trying to address.

Action Language C+
Answer Set Programming
Incremental Execution
Multi-modal Translation
Efficient Computation
Innovation

Methods, ideas, or system contributions that make the work stand out.

Answer Set Programming
Action Language C+
Incremental Grounding
Module Theorem
Multi-modal Translation
🔎 Similar Papers
No similar papers found.
💼 Related Jobs
No related jobs found.
J
Joseph Babb
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, USA
Joohyung Lee
Joohyung Lee
Associate Professor, Arizona State University
Artificial IntelligenceKnowledge RepresentationMachine LearningNeuro-Symbolic AILogic