Planning with Qualitative Action-Trajectory Constraints in PDDL
Authors: Luigi Bonassi, Alfonso Emilio Gerevini, Enrico Scala
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we experimentally study the usefulness of our action-trajectory constraints as a tool to express control knowledge. The experimental results show that the performance of a classical planner can be significantly improved by exploiting knowledge expressed by action constraints and handled by our compilation |
| Researcher Affiliation | Academia | Luigi Bonassi , Alfonso Emilio Gerevini and Enrico Scala Dipartimento di Ingegneria dell Informazione, Universit a degli Studi di Brescia, Italy |
| Pseudocode | Yes | Algorithm 1: PAC-C |
| Open Source Code | Yes | Full proof in the supplementary material.3 3Supplementary material, benchmark domains and Python implementation of PAC-C can be found at https://bit.ly/3kerz8s. |
| Open Datasets | Yes | Since there are no available benchmarks featuring action constraints, we generated a new benchmark suite3 starting from the problems of the 5th International Planning Competition. ... 3Supplementary material, benchmark domains and Python implementation of PAC-C can be found at https://bit.ly/3kerz8s. |
| Dataset Splits | No | The paper describes using a benchmark suite for evaluation and does not specify training, validation, or test dataset splits in the conventional machine learning sense. |
| Hardware Specification | Yes | All experiments ran on an Xeon Gold 6140M 2.3 GHz, with time and memory limits of 1800s and 8GB, respectively. |
| Software Dependencies | No | The paper mentions software like LAMA, PACC3, TCORE, and LTL-C, but it does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | All experiments ran on an Xeon Gold 6140M 2.3 GHz, with time and memory limits of 1800s and 8GB, respectively. |