Competing for Resources: Estimating Adversary Strategy for Effective Plan Generation
Authors: Lukáš Chrpa, Pavel Rytíř, Rostislav Horčík, Stefan Edelkamp9707-9715
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | 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 {lukas.chrpa, pavel.rytir, rostislav.horcik, stefan.edelkamp}@fel.cvut.cz |
| 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. |