On the Complexity and Expressiveness of Automated Planning Languages Supporting Temporal Reasoning

Abstract

Automated planning is an important area of Artificial Intelligence, which has been thoroughly developed in the last decades. In recent years, a significant amount of research has focused on planning languages and systems supporting temporal reasoning, recognizing its importance in modeling and solving real-world complex tasks. Many such languages are action-based, i.e. they model planning problems by specifying which actions can be executed at any given time to affect the environment. Timeline-based planning, a different paradigm originally introduced to support planning and scheduling of space operations, models planning domains as systems composed of a set of independent, but interacting, components, whose behavior over time, the timelines, is governed by a set of temporal constraints. A thorough theoretical study of timeline-based planning languages, and a rigorous comparison with action-based languages, are still missing. We outline recent results and future directions on this front.