FLECS: Planning with a Flexible Commitment Strategy

Abstract

There has been evidence that least-commitment planners can efficiently handle planning problems that involve difficult goal interactions. This evidence has led to the common belief that delayed-commitment is the "best" possible planning strategy. However, we recently found evidence that eager-commitment planners can handle a variety of planning problems more efficiently, in particular those with difficult operator choices. Resigned to the futility of trying to find a universally successful planning strategy, we devised a planner that can be used to study which domains and problems are best for which planning strategies. In this article we introduce this new planning algorithm, flecs, which uses a FLExible Commitment Strategy with respect to plan-step orderings. It is able to use any strategy from delayed-commitment to eager-commitment. The combination of delayed and eager operator-ordering commitments allows flecs to take advantage of the benefits of explicitly using a simulated execution state and reasoning about planning constraints. flecs can vary its commitment strategy across different problems and domains, and also during the course of a single planning problem. flecs represents a novel contribution to planning in that it explicitly provides the choice of which commitment strategy to use while planning. FLECS provides a framework to investigate the mapping from planning domains and problems to efficient planning strategies.

Cite

Text

Veloso and Stone. "FLECS: Planning with a Flexible Commitment Strategy." Journal of Artificial Intelligence Research, 1995. doi:10.1613/JAIR.131

Markdown

[Veloso and Stone. "FLECS: Planning with a Flexible Commitment Strategy." Journal of Artificial Intelligence Research, 1995.](https://mlanthology.org/jair/1995/veloso1995jair-flecs/) doi:10.1613/JAIR.131

BibTeX

@article{veloso1995jair-flecs,
  title     = {{FLECS: Planning with a Flexible Commitment Strategy}},
  author    = {Veloso, Manuela M. and Stone, Peter},
  journal   = {Journal of Artificial Intelligence Research},
  year      = {1995},
  pages     = {25-52},
  doi       = {10.1613/JAIR.131},
  volume    = {3},
  url       = {https://mlanthology.org/jair/1995/veloso1995jair-flecs/}
}