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.
Multiagent Connected Path Planning: PSPACE-Completeness and How to Deal With It
Authors: Davide Tateo, Jacopo Banfi, Alessandro Riva, Francesco Amigoni, Andrea Bonarini
AAAI 2018 | Venue PDF | LLM Run Details | Input Tokens: 15,865 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,931 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 | Furthermore, we present three algorithms adopting different search paradigms, and we empirically show that they may efficiently obtain a feasible plan, if any exists, in different settings. |
| Researcher Affiliation | Academia | Davide Tateo, Jacopo Banfi, Alessandro Riva, Francesco Amigoni, Andrea Bonarini Dipartimento di Elettronica, Informazione e Bioingegneria Politecnico di Milano Piazza Leonardo da Vinci, 32, Milano, Italy EMAIL |
| Pseudocode | Yes | Algorithm 1: Sample-Based |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper uses 'two realistic environments employed by (Hollinger and Singh 2012)' and then states 'For each of the experimental setting, we randomly generate 50 start-goal states.' It does not provide concrete access information (link, DOI, repository, or formal citation for a public dataset) for the generated data. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages or sample counts) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | The number of samples in Ω for SB and RSB is fixed to 100 for each iteration, as in (Rooker and Birk 2007), while the randomization exponent of Equation (1) is set to δ = 3 (from preliminary experiments). |