System ASPMT2SMT:Computing ASPMT Theories by SMT Solvers
This work provides a practical tool for researchers and practitioners in AI and logic programming to solve ASPMT problems more efficiently, though it is incremental as it implements an existing theoretical translation.
The paper tackles the problem of computing stable models for Answer Set Programming Modulo Theories (ASPMT) by developing a compiler called aspsmt2smt that translates tight ASPMT programs into SMT instances, enabling the use of SMT solvers like z3 for efficient reasoning, and demonstrates its effectiveness in handling real number computations for continuous changes.
Answer Set Programming Modulo Theories (ASPMT) is an approach to combining answer set programming and satisfiability modulo theories based on the functional stable model semantics. It is shown that the tight fragment of ASPMT programs can be turned into SMT instances, thereby allowing SMT solvers to compute stable models of ASPMT programs. In this paper we present a compiler called {\sc aspsmt2smt}, which implements this translation. The system uses ASP grounder {\sc gringo} and SMT solver {\sc z3}. {\sc gringo} partially grounds input programs while leaving some variables to be processed by {\sc z3}. We demonstrate that the system can effectively handle real number computations for reasoning about continuous changes.