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

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 {davide.tateo, jacopo.banfi, alessandro.riva, francesco.amigoni, andrea.bonarini}@polimi.it
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).