Restricted Domains of Dichotomous Preferences with Possibly Incomplete Information
Authors: Zoi Terzopoulou, Alexander Karpov, Svetlana Obraztsova5726-5733
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We obtain forbidden subprofile characterisations for various important dichotomous domains. Then, we are concerned with incomplete profiles that may arise in many real-world scenarios, where we have partial information about the voters preferences. We tackle the problem of determining whether an incomplete profile admits a completion within a certain restricted domain and design constructive polynomial algorithms to that effect. |
| Researcher Affiliation | Academia | Zoi Terzopoulou,1 Alexander Karpov,2 and Svetlana Obraztsova3 1Institute for Logic, Language and Computation, University of Amsterdam, The Netherlands 2HSE University, Institute of Control Sciences of Russian Academy of Sciences, Russia 3Nanyang Technological University, Singapore |
| Pseudocode | Yes | The algorithm SVEI-INCOMPLETE is based on three subalgorithms, viz., FILLING, GRAPH, and ORDERING. FILLING(P): First set Pf to P. For an arbitrary cell of unknown value pi,j in Pf, check whether the matrices Pf[pi,j|0] and Pf[pi,j|1] contain the forbidden pattern X for SVEI. If they both do, announce invalid and exit; if neither does, continue to the next cell of unknown value in Pf; if only one the matrix Pf[pi,j|x] for x {0, 1} does, then set Pf to Pf[pi,j|1 x] and continue to the next cell of unknown value in Pf. When no other cell of unknown value remains to be considered, return the profile Pf. Repeat this process nm times each repetition ensures that at least one cell that can be filled will indeed be filled, and the thus order of the selected cell does not matter overall. |
| Open Source Code | No | The paper does not provide any statement about making its source code publicly available, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and characterization theorems. It does not describe experiments using datasets for training, and therefore does not provide access information for a training dataset. |
| Dataset Splits | No | The paper describes theoretical contributions and algorithms. It does not include empirical experiments with dataset splits for validation. |
| Hardware Specification | No | The paper focuses on theoretical contributions and algorithm design. It does not mention any specific hardware used for running experiments. |
| Software Dependencies | No | The paper focuses on theoretical contributions and algorithm design. It does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical contributions and algorithm design and does not describe any empirical experiments, thus no experimental setup details like hyperparameters are provided. |