Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Competing for Resources: Estimating Adversary Strategy for Effective Plan Generation
Authors: Lukáš Chrpa, Pavel Rytíř, Rostislav Horčík, Stefan Edelkamp9707-9715
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluate our approach leveraging sampling of competitor s actions by comparing it to the naive approach optimising the make-span (not taking the competing agent into account at all) and to Nash Equilibrium (mixed) strategies. |
| Researcher Affiliation | Academia | Faculty of Electrical Engineering, Czech Technical University in Prague EMAIL |
| Pseudocode | Yes | Algorithm 1: Estimating earliest action application and fact occurrence time; Algorithm 2: Estimating adversary strategy |
| Open Source Code | Yes | Code and benchmarks can be found at https://gitlab.com/FRASProject/aaai22-competing-for-resources |
| Open Datasets | No | The paper mentions using "Resource Hunting domain" and "Taxi domain" as case studies for experiments but does not provide specific access information (link, DOI, citation with authors/year) for these datasets to be publicly available. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | Yes | We ran the experiments on Linux with 2.10GHz Intel Xeon CPU E5-2620 v4 with 32GB RAM. |
| Software Dependencies | No | The paper mentions software like "PDDL 2.1", "Temporal Fast Downward (Eyerich, Mattm uller, and R oger 2009)", "Fast Downward planner (Helmert 2006)", and "CPT4 (Vidal 2011)" but does not provide specific version numbers for these software dependencies or libraries. |
| Experiment Setup | No | The paper describes the general experimental setup (e.g., comparison methods, domains, planner choices) but does not provide specific hyperparameter values, training configurations, or detailed system-level settings used for the experiments. |