In a planning task, an agent must choose the most efficient action from a potentially large set of actions at each step. During a heuristic search, logic-based planners use preferred operators to reduce the branching factor significantly. This work presents a method for sampling and learning preferred operators, aiming for their applicability across the entire state space of a planning task. We demonstrate that these learned preferred operators have competitive results compared to the current best logic-based approach. Our objective is to identify ideal preferred operators, situated along the shortest paths leading to some goal. However, due to the huge size of search state spaces, we introduce a novel sampling strategy tailored for extracting preferred operators. Our research shows we can obtain high-quality preferred operators from a sample set covering a fraction of the state space. To understand this new category of preferred operators, we conduct controlled experiments using planning tasks where we have access to the entire state space with perfect cost-to-goal estimates. We systematically compare the proposed approach to baselines, evaluate the effectiveness of learned preferred operators learned from several sample set sizes, and assess their performance when combined with different heuristic functions.
- Marcus Rolf Peter Ritt (main)
- André Grahl Pereira (coadvisor)
- Felipe Rech Meneguzzi (University of Aberdeen)
- Ramon Fraga Pereira (University of Manchester)
- Edison Pignaton de Freitas (Federal University of Rio Grande do Sul)
Use latexmk ppgc-diss-pprobst.tex for easy compilation.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Generic License.