Behavior of Analogical Inference w.r.t. Boolean Functions
Authors: Miguel Couceiro, Nicolas Hug, Henri Prade, Gilles Richard
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This result is confirmed by an empirical study showing that the upper bound is tight. It highlights the specificity of analogical inference, also characterized in terms of the Hamming distance. |
| Researcher Affiliation | Academia | Miguel Couceiro1, Nicolas Hug2, Henri Prade2,3, Gilles Richard2 1. University of Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France 2. IRIT, University of Toulouse, France 3. QCIS, University of Technology, Sydney, Australia |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | No | The paper does not provide concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset. It refers to 'X = B8' as the domain for its functions, implying synthetic data generation or conceptual space. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. It mentions using 'a random S of size |X|/m' for experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For X = B8, we started from 3 affine functions: 1. g1(x) = x1 which is the first projection. 2. g2(x) = x1 + x2 (all variables but x1, x2 are irrelevant) 3. g3(x) = Σm i=1xi where all variables are relevant. To get an ε-close function, we just flip a random fraction ε of the values of gi on the universe X. Doing so, we are sure to get a function fi,ε such that d(fi,ε, gi) ε and then d(fi,ε, L) ε. ... For each approximately affine function fi,ε, we performed 100 experiments with a random S of size |X|/m. |