Temporal Planning with Temporal Metric Trajectory Constraints

Authors: Andrea Micheli, Enrico Scala7675-7682

AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Our experiments prove the proposed framework superior to alternative state-of-the-art planning approaches on industrial benchmarks, and competitive with similar solving methods on well known benchmarks took from the planning competition.
Researcher Affiliation Academia Andrea Micheli Fondazione Bruno Kessler Trento, 38123, Italy amicheli@fbk.eu Enrico Scala Fondazione Bruno Kessler Trento, 38123, Italy escala@fbk.eu
Pseudocode Yes Algorithm 1 EAGER-SEARCH Successor Function
Open Source Code Yes TPACK and the benchmark instances are available at http://es.fbk.eu/people/amicheli/resources/aaai19.
Open Datasets Yes TPACK and the benchmark instances are available at http://es.fbk.eu/people/amicheli/resources/aaai19. We took 6 domains from the IPC-14 competition (Vallati, Chrpa, and Mc Cluskey 2018), choosing those where a problem generator was available.
Dataset Splits No The paper describes the instances used for evaluation (e.g., number of items, jobs, domains from IPC-14) but does not provide explicit training/validation/test dataset splits or methodologies for creating such splits.
Hardware Specification Yes We ran all the experiments on a Xeon E5-2620 2.10GHz with 1800s/15GB of time/memory limits.
Software Dependencies No The paper mentions specific planners used (ENHSP, ITSAT, TFD, OPTIC) but does not provide their version numbers or any other software dependencies with version specifications.
Experiment Setup Yes All configurations ran using a best-first-search with f(s) = g(s) + 4 h(s) where the g-value is the length of the prefix.