A Planning Domain Definition Language Generator, Interpreter, and Knowledge Base for Efficient Automated Planning

Abstract

AbstractThe Planning Domain Definition Language (PDDL) successfully encodes classical planning tasks by easily describing objects, actions, and states in many planning domains. PDDL also describes domains, but they include only predefined sets of actions that can solve problems in a finite set of states. Indeed, the PDDL structure disables the processing of single predicates and operators. As a consequence, they cannot be arbitrarily composed to model new domains. To overcome these limitations, we propose a domain-independent, general-purpose knowledge design and task planning system based on the combination of a PDDL generator and interpreter and a Knowledge Base. The former builds planning data structures, where every object is a PDDL token independent of its original domain. It also allows merging these objects to formulate new PDDL domains and problems, ensuring consistency and validity of generated definitions. Their resolution is based on a powerful object-based reasoning instead of an inefficient lexical-based one. The latter contains the necessary relationships and representations to allow data storing and reusability. Their combination enables the storage, interpretation, and reuse of planning data, resulting in integration between the planning process and description logic reasoning. The overall system guarantees a flexible adaptation of the computed planning domains to changing environmental conditions, agent capabilities, and assigned tasks, promoting effective sharing and reuse of domain knowledge across different systems and applications.