Computational methods for Dynamic Answer Set Programming

TL;DR

This paper extends Answer Set Programming (ASP) to better handle dynamic, temporal, and metric problems, enabling more effective reasoning in complex, real-world industrial scenarios.

Contribution

It introduces a novel integration of dynamic, temporal, and metric logics into ASP, enhancing its capability to model and solve dynamic problems.

Findings

Developed a new ASP framework for dynamic domains

Demonstrated improved reasoning efficiency in dynamic scenarios

Enhanced modeling capabilities for complex temporal problems

Abstract

In our daily lives and industrial settings, we often encounter dynamic problems that require reasoning over time and metric constraints. These include tasks such as scheduling, routing, and production sequencing. Dynamic logics have traditionally addressed these needs but often lack the flexibility and integration required for comprehensive problem modeling. This research aims to extend Answer Set Programming (ASP), a powerful declarative problem-solving approach, to handle dynamic domains effectively. By integrating concepts from dynamic, temporal, and metric logics into ASP, we seek to develop robust systems capable of modeling complex dynamic problems and performing efficient reasoning tasks, thereby enhancing ASPs applicability in industrial contexts.