In automated planning the ability of expressing constraints on the structure of the desired plans is important to deal with solution quality, as well as to express control knowledge. In PDDL3 this is supported through state-trajectory constraints corresponding to a class of LTLf formulae. In this paper, first we introduce a formalism to express trajectory constraints over actions in the plan, rather than over traversed states; the new class of constraints retains the same temporal modal operators of PDDL3, and adds two useful modalities. Then we investigate compilation-based methods to deal with action-trajectory constraints in propositional planning, and propose a new simple effective method. Finally, we experimentally study the usefulness of our action-trajectory constraints as a tool to express control knowledge. The experimental results show that the performance of a classical planner can be significantly improved by exploiting knowledge expressed by action constraints and handled by our compilation, while the same knowledge turns out to be less beneficial when specified as state constraints and handled by two state-of-the-art systems supporting state constraints.
Planning with Qualitative Action-Trajectory Constraints in PDDL
Bonassi L.;Gerevini A. E.;Scala E.
2022-01-01
Abstract
In automated planning the ability of expressing constraints on the structure of the desired plans is important to deal with solution quality, as well as to express control knowledge. In PDDL3 this is supported through state-trajectory constraints corresponding to a class of LTLf formulae. In this paper, first we introduce a formalism to express trajectory constraints over actions in the plan, rather than over traversed states; the new class of constraints retains the same temporal modal operators of PDDL3, and adds two useful modalities. Then we investigate compilation-based methods to deal with action-trajectory constraints in propositional planning, and propose a new simple effective method. Finally, we experimentally study the usefulness of our action-trajectory constraints as a tool to express control knowledge. The experimental results show that the performance of a classical planner can be significantly improved by exploiting knowledge expressed by action constraints and handled by our compilation, while the same knowledge turns out to be less beneficial when specified as state constraints and handled by two state-of-the-art systems supporting state constraints.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
