Fast Planning in Domains with Derived Predicates: An Approach Based on Rule-Action Graphs and Local Search
Abstract
The ability to express in the formalization of a planning domain is both practically and theoretically important. In this paper, we propose an approach to planning with derived predicates where the search space consists of Rule-Action Graphs, particular graphs of actions and rules representing derived predicates. We present some techniques for representing rules and reasoning with them, which are integrated into a method for planning through local search and rule-action graphs. We also propose some new heuristics for guiding the search, and some experimental results illustrating the performance of our approach. Our proposed techniques are implemented in a planner that took part in the fourth International Planning Competition showing good performance in many benchmark problems.
Cite
Text
Gerevini et al. "Fast Planning in Domains with Derived Predicates: An Approach Based on Rule-Action Graphs and Local Search." AAAI Conference on Artificial Intelligence, 2005.Markdown
[Gerevini et al. "Fast Planning in Domains with Derived Predicates: An Approach Based on Rule-Action Graphs and Local Search." AAAI Conference on Artificial Intelligence, 2005.](https://mlanthology.org/aaai/2005/gerevini2005aaai-fast/)BibTeX
@inproceedings{gerevini2005aaai-fast,
title = {{Fast Planning in Domains with Derived Predicates: An Approach Based on Rule-Action Graphs and Local Search}},
author = {Gerevini, Alfonso and Saetti, Alessandro and Serina, Ivan and Toninelli, Paolo},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2005},
pages = {1157-1162},
url = {https://mlanthology.org/aaai/2005/gerevini2005aaai-fast/}
}