Projection, Inference, and Consistency
Authors: John N. Hooker
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper focuses on theoretical aspects of projection in logic and constraint programming, presenting concepts, theorems (e.g., Theorem 2, Theorem 3), and an algorithm (Algorithm 1) for solving combinatorial projection problems. It discusses methods like Benders decomposition and Fourier-Motzkin elimination from a conceptual and algorithmic perspective, without conducting empirical studies, evaluating on datasets, or reporting experimental metrics within the paper itself. |
| Researcher Affiliation | Academia | J. N. Hooker Carnegie Mellon University, Pittsburgh, USA jh38@andrew.cmu.edu |
| Pseudocode | Yes | Algorithm 1 Given a projection of alldiff(xn) onto xk, compute a projection onto xk 1. The projection onto xk is assumed to be a disjunction of constraint sets, each of which has the form (3). The algorithm is applied to each disjunct, after which the disjunction of all created constraint sets forms the projection onto xk 1. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using publicly available datasets for training. It uses examples (e.g., Table 1, Figure 2) but not in the context of dataset access for reproducibility. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used, as it focuses on theoretical contributions and algorithms rather than empirical evaluation. |
| Software Dependencies | No | The paper mentions 'state-of-the-art propositional satisfiability (SAT) solvers' but does not specify any software names with version numbers or other key dependencies required for reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe any specific experimental setup details, hyperparameters, or training configurations. |