Computing Contingent Plans via Fully Observable Non-Deterministic Planning

Authors: Christian Muise, Vaishak Belle, Sheila McIlraith

AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We compared PO-PRP with the state of the art in offline conditional planning, CLG (Albore, Palacios, and Geffner 2009), to assess both the efficiency of the solving process and succinctness of the generated plans. We measure the efficiency in terms of the time it takes to compute a complete solution, and the succinctness in terms of the generated conditional plan. All experiments were conducted on a Linux desktop with a 3.4GHz processor, with time / memory limits of 1hr / 2GB respectively.
Researcher Affiliation Academia Christian Muise, Vaishak Belle, and Sheila A. Mc Ilraith Department of Computer Science University of Toronto, Toronto, Canada. {cjmuise,vaishak,sheila}@cs.toronto.edu
Pseudocode No The paper describes procedures in text but does not include any clearly labeled pseudocode or algorithm blocks.
Open Source Code Yes All problems, example plans, and PO-PRP source is available online at, http://www.haz.ca/research/poprp/
Open Datasets Yes Aside from ctp-ch, we retrieved all problems from the benchmark set that is included with the online contingent planner of BG, K-Planner (Bonet and Geffner 2011).
Dataset Splits No The paper does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts).
Hardware Specification Yes All experiments were conducted on a Linux desktop with a 3.4GHz processor, with time / memory limits of 1hr / 2GB respectively.
Software Dependencies No The paper mentions specific tools and representations (e.g., PDDL, SAS+, PRP, CLG) but does not provide specific version numbers for any software dependencies or libraries used in the experiments.
Experiment Setup Yes All experiments were conducted on a Linux desktop with a 3.4GHz processor, with time / memory limits of 1hr / 2GB respectively.