Attribute grammars with set attributes and global constraints as a unifying framework for planning domain models

Abstract

The paper presents attribute grammars as a unifying framework for modeling planning domains and problems. The motivation is to exploit techniques from formal languages in domain model verification, plan and goal recognition, domain model acquisition, as well as in planning. Grammar rules are used for action selection while specific set attributes are used to collect events (preconditions and effects of actions) that are ordered using a global timeline constraint. We show how classical STRIPS, hierarchical task networks, and procedural domain models are transformed to attribute grammars.