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..
Planning with Participation Constraints
Authors: Hanrui Zhang, Yu Cheng, Vincent Conitzer5260-5267
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result is a fully polynomial-time approximation scheme (FPTAS) for planning with participation constraints. We obtain this FPTAS by ο¬rst designing an exponential-time algorithm (Algorithm 1 in Section 3.1) which computes an exact optimal policy, and then carefully discretizing the algorithm without violating participation constraints (Section 3.2). |
| Researcher Affiliation | Academia | Hanrui Zhang1, Yu Cheng2, Vincent Conitzer3 1 Carnegie Mellon University 2 University of Illinois at Chicago 3 Duke University EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Algorithm for computing the Pareto frontier of the principal/agent s utility for all states. Algorithm 2: subcurve(s, a): algorithm for computing the subcurve for a state-action pair. Algorithm 3: Algorithm for executing the optimal policy on the ο¬y (i.e., choosing actions on the ο¬y) |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is available. |
| Open Datasets | No | The paper describes theoretical algorithms and their application to problem formulations (e.g., ride-hailing, screening policies) but does not conduct empirical experiments using datasets, thus no public dataset access information is provided. |
| Dataset Splits | No | The paper describes theoretical algorithms and their application to problem formulations but does not conduct empirical experiments using datasets, thus no dataset split information (training, validation, test) is provided. |
| Hardware Specification | No | The paper describes theoretical algorithms and their properties, not empirical experiments, and therefore does not specify any hardware used. |
| Software Dependencies | No | The paper describes theoretical algorithms and their properties, not empirical experiments, and therefore does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper describes theoretical algorithms and their properties, not empirical experiments, and therefore does not provide details about an experimental setup, hyperparameters, or training settings. |