Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Planning with Qualitative Action-Trajectory Constraints in PDDL
Authors: Luigi Bonassi, Alfonso Emilio Gerevini, Enrico Scala
IJCAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 16,944 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,545 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |