CNL2ASP: converting controlled natural language sentences into ASP
This work addresses the usability barrier for non-expert users in specifying combinatorial problems in ASP, though it is incremental as it builds on existing CNL and ASP translation methods.
The authors tackled the challenge of making Answer Set Programming (ASP) more accessible by developing CNL2ASP, a tool that converts controlled natural language sentences into ASP, and demonstrated that it achieves satisfactory performance on real-world benchmarks compared to manually written encodings.
Answer Set Programming (ASP) is a popular declarative programming language for solving hard combinatorial problems. Although ASP has gained widespread acceptance in academic and industrial contexts, there are certain user groups who may find it more advantageous to employ a higher-level language that closely resembles natural language when specifying ASP programs. In this paper, we propose a novel tool, called CNL2ASP, for translating English sentences expressed in a controlled natural language (CNL) form into ASP. In particular, we first provide a definition of the type of sentences allowed by our CNL and their translation as ASP rules, and then exemplify the usage of the CNL for the specification of both synthetic and real-world combinatorial problems. Finally, we report the results of an experimental analysis conducted on the real-world problems to compare the performance of automatically generated encodings with the ones written by ASP practitioners, showing that our tool can obtain satisfactory performance on these benchmarks. Under consideration in Theory and Practice of Logic Programming (TPLP).