RI: Small: Automated Optimization of Programs and Processing Tools in Answer Set Programming

The project aims to address critical outstanding challenges in answer set programming, a leading knowledge representation/declarative constraint programming paradigm. Answer set programming has its roots in the need to support fast design of robust and reliable software solutions for complex knowledge-intensive applications. It reduces the programming task to modeling an application domain as a theory in a language of logic, and leaves all computational concerns to automated reasoning. Answer set programming has been successfully used in scientific and industrial applications. Examples include product configuration, decision support systems for space shuttle flight controllers, repairing large-scale biological networks. However, successful applications notwithstanding, this computational paradigm is far from realizing its full potential. Selecting the right combination of problem representation and a reasoning tool is nothing but straightforward. As a result fine-tuning the encoding and tools to obtain acceptable performance requires both significant expertise and effort. The PIs propose to address this challenge by developing automated program rewriting techniques and coupling them with advanced portfolio solving as well as parameter tuning technologies.