Plan Synthesis for Knowledge and Action Bases

Authors: Diego Calvanese, Marco Montali, Fabio Patrizi, Michele Stawowy

IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental To test the feasibility of our proposal for knowledge-intensive planning, we ran a preliminary empirical evaluation of the framework of Section 6. We considered a DL-Lite A-e KAB over the domain of Example 1, translated it into ADL, and fed it as input via PDDL to an off-the-shelf planner (we used Fast Downward3). We varied the difficulty by increasing the bound on the object domain, thus affecting the number of ground atoms for the planner. With 9 domain elements, resulting in 2 000 atoms, a plan was found in under 1s. For 30 elements ( 300 000 atoms) a plan was found in 120s, while for 35 elements ( 1 200 000 atoms) the planner timed out.
Researcher Affiliation Academia Diego Calvanese, Marco Montali, Fabio Patrizi Free Univ. of Bozen-Bolzano, Italy lastname@inf.unibz.it Michele Stawowy IMT Lucca, Italy michele.stawowy@imtlucca.it
Pseudocode Yes Algorithm 1 Forward planning algorithm schema; Algorithm 2 Plan synthesis for state-bounded e KABs.
Open Source Code No The paper does not provide an explicit statement about open-sourcing its own code or a link to a repository for the described methodology. It mentions using 'Fast Downward' which is a third-party tool.
Open Datasets No The paper does not explicitly state that a public or open dataset was used. It refers to an internal example (Example 1) without providing access information for any associated data.
Dataset Splits No The paper does not provide specific training/test/validation dataset splits. The empirical evaluation is described as a 'preliminary empirical evaluation' on a self-defined domain.
Hardware Specification No The paper does not explicitly describe the hardware used to run its experiments. It only mentions using an 'off-the-shelf planner'.
Software Dependencies No The paper mentions using 'Fast Downward' but does not provide a specific version number for this software or any other key software components used in the experiments.
Experiment Setup No The paper does not provide specific details about the experimental setup, such as hyperparameters or system-level training settings. It only mentions varying the 'bound on the object domain' and using an 'off-the-shelf planner'.