RT Journal Article
T1 The IBaCoP planning system: instance-based configured portfolios
A1 Cenamor Guijarro, Isabel Rosario
A1 Rosa Turbides, Tomás Eduardo de la
A1 Fernández Rebollo, Fernando
AB Sequential planning portfolios are very powerful in exploiting the complementary strength of different automated planners. The main challenge of a portfolio planner is to define which base planners to run, to assign the running time for each planner and to decide in what order they should be carried out to optimize a planning metric. Portfolio configurations are usually derived empirically from training benchmarks and remain fixed for an evaluation phase. In this work, we create a per-instance configurable portfolio, which is able to adapt itself to every planning task. The proposed system pre-selects a group of candidate planners using a Pareto-dominance filtering approach and then it decides which planners to include and the time assigned according to predictive models. These models estimate whether a base planner will be able to solve the given problem and, if so, how long it will take. We define different portfolio strategies to combine the knowledge generated by the models. The experimental evaluation shows that the resulting portfolios provide an improvement when compared with non-informed strategies. One of the proposed portfolios was the winner of the Sequential Satisficing Track of the International Planning Competition held in 2014.
PB AI Access Foundation
SN 1076-9757
YR 2016
FD 2016-08-01
LK https://hdl.handle.net/10016/29809
UL https://hdl.handle.net/10016/29809
LA eng
NO We thank the authors of the base planners because our work is based largely on their previous effort. This work has been partially supported by the Spanish projects TIN2011-27652-C03-02, TIN2012-38079-C03-02 and TIN2014-55637-C2-1-R.
DS e-Archivo
RD 17 jul. 2024