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.131Markdown
[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.131BibTeX
@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/}
}