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.
Fact-Alternating Mutex Groups for Classical Planning
Authors: Daniel Fišer, Antonín Komenda
JAIR 2018 | Venue PDF | LLM Run Details | Input Tokens: 52,285 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,993 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 | The experimental evaluation of the pruning algorithm shows a substantial increase in a number of solved tasks in domains from the optimal deterministic track of the last two planning competitions (IPC 2011 and 2014). |
| Researcher Affiliation | Academia | Daniel Fiˇser EMAIL Anton ın Komenda EMAIL Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague 6, Czech Republic 166 27 |
| Pseudocode | Yes | Algorithm 1: MAXIMUM-MUTEX-GROUP ... Algorithm 2: Inference of fact-alternating mutex groups using ILP. ... Algorithm 3: Pruning of a planning task using inferred fam-groups. |
| Open Source Code | Yes | All algorithms experimentally evaluated in this section were implemented in the Fast Downward s preprocessor3 (Helmert, 2006) written in Python programming language. Algorithm 2 and the h2 heuristic were implemented as C extensions for Python. (...) 3. https://github.com/danfis/fast-downward, branch jair-fa-mutex |
| Open Datasets | Yes | The algorithms were evaluated on all domains from the optimal deterministic track of the International Planning Competition (IPC) 2011 and 2014 that do not contain any conditional effects after grounding (i.e., all except the Citycar domain from IPC 2014). |
| Dataset Splits | No | The paper uses standard benchmark domains from the International Planning Competition (IPC) 2011 and 2014, but it does not explicitly provide specific dataset split percentages, sample counts, or a detailed methodology for creating new dataset splits within the paper. It refers to 'tasks' within these domains. |
| Hardware Specification | Yes | The experiments were run on a computer with an Intel Xeon E5-4617 2.9GHz processor and a memory limit set to 8 GB RAM. |
| Software Dependencies | Yes | The algorithm for inference of fam-groups (Algorithm 2) was implemented using a CPLEX ILP solver (v12.6.1.0) running with default configuration in one thread. |
| Experiment Setup | Yes | The algorithm for inference of fam-groups (Algorithm 2) was implemented using a CPLEX ILP solver (v12.6.1.0) running with default configuration in one thread. ... The maximal allowed time for the whole planning process (including preprocessor and search) was set to four hours and the maximal memory limit was set to 8 GB. |