Lossy Compression of Pattern Databases Using Acyclic Random Hypergraphs
Authors: Mehdi Sadeqi, Howard J. Hamilton
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | ARHC shows higher performance than level-by-level Bloom filter PDB compression in all experiments conducted so far. The experimental results show that ARHC performs substantially better than a level-by-level Bloom filter PPDB with respect to three measures. |
| Researcher Affiliation | Academia | Mehdi Sadeqi and Howard J. Hamilton Department of Computer Science, University of Regina, Canada {sadeqi2m,hamilton}@cs.uregina.ca |
| Pseudocode | No | The paper describes procedures in numbered steps (e.g., 'The ARHC procedure can be summarized as follows:'), but these are not formatted as pseudocode or labeled as an algorithm block. |
| Open Source Code | No | The paper does not include any statement about open-source code availability or links to a code repository for the described methodology. |
| Open Datasets | Yes | Experimental results in three problem domains, Sliding-Tile Puzzle, Blocks World with Table Positions, and Scanalyzer, are presented in this section... They are specified using production system vector notation (PSVN) [Holte et al., 2014] and are the same problem domains and representations used for experiments in [Sadeqi and Hamilton, 2016]. |
| Dataset Splits | No | The paper uses '100,000 uniformly generated random problem instances' and '1,000 uniformly generated random problem instances' for evaluation but does not specify formal train, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for the experiments, such as CPU or GPU models. |
| Software Dependencies | No | The paper mentions 'IDA*' and 'Zobrist hash functions' but does not provide specific version numbers for any software dependencies or libraries used in the implementation or experiments. |
| Experiment Setup | Yes | an integer number n is chosen such that n is the smallest integer number greater than or equal to 1.23m where n mod 3 = 0. A table T is then constructed with n entries. Each entry in T is represented by b or more bits where b = log2(v + 2) (b bits for ARHC-Base and more than b bits for ARHC-Extended). In the ARHC-Extended method, we dedicate c > b = log2(v +2) bits to each entry in the lookup table and calculate (T[h1(s)] + T[h2(s)] + T[h3(s)]) modulo 2c rather than modulo 2b. |