When Quality Matters: Constraint Programming for Automated Temporal and Numeric Planning
Résumé
Automated planning is a field of Artificial Intelligence interested in finding a set of actions that drives the evolution of the environment from an initial state to a desired goal state. Much of the work of the community has been on socalled domain-independent planning where a solver is expected to produce a plan from an abstract problem, specified in a common description language. This has led the community to produce a number of highly-efficient solvers that can be expected to work on a large variety of domains without any fine-tuning. Where most of the work has focused on sequential plans over purely symbolic states, we instead propose a constraint-based planner whose focus is on more expressive variants, namely numeric and temporal planning, essential in many practical applications. We extend an existing CP encoding of temporal planning with support for optimization and numeric states and leverage an existing lazy clause generation CP solver to find and optimize plans. Where the most successful automated planners rely on some form of forward-search, we show that constraintprogramming can be just as effective in finding satisfiable solutions while substantially improving the quality of the produced plans
Domaines
| Origine | Fichiers produits par l'(les) auteur(s) |
|---|---|
| Licence |
