Novelty Heuristics, Multi-Queue Search, and Portfolios for Numeric Planning
This work addresses performance bottlenecks in numeric planning for AI researchers, but it is incremental as it builds on existing heuristic search methods.
The paper tackled the problem of improving heuristic search performance in numeric planning by introducing numeric novelty heuristics, Manhattan distance heuristic, multi-queue search, and portfolios, resulting in enhanced solving capabilities for planning tasks.
Heuristic search is a powerful approach for solving planning problems and numeric planning is no exception. In this paper, we boost the performance of heuristic search for numeric planning with various powerful techniques orthogonal to improving heuristic informedness: numeric novelty heuristics, the Manhattan distance heuristic, and exploring the use of multi-queue search and portfolios for combining heuristics.