Planning with Participation Constraints
Authors: Hanrui Zhang, Yu Cheng, Vincent Conitzer5260-5267
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | 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 first 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 hanruiz1@cs.cmu.edu, yucheng2@uic.edu, conitzer@cs.duke.edu |
| 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 fly (i.e., choosing actions on the fly) |
| 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. |