Auditable Algorithms for Approximate Model Counting

Authors: Kuldeep S. Meel, Supratik Chakraborty, S. Akshay

AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical The design of algorithms has traditionally focused on optimizing resources like time, space, or communication without necessarily considering how hard it is to independently certify correctness of results. ... Our proofs involve reasoning about specially constructed formulas with qantifier alternations, with incomplete information about parts of the formula. This is quite challenging in general, and one of our technical novelties lies in our application of the probabilistic method in the proofs.
Researcher Affiliation Academia Kuldeep S. Meel1 r Supratik Chakraborty2 r S. Akshay2 1 University of Toronto, Canada 2 Indian Institute of Technology Bombay, Mumbai, India
Pseudocode Yes Algorithm 1: Stock(F) ... Algorithm 2: Stock Audit(F, Stock(F)) ... Algorithm 3: Equal Cells Counter(F) ... Algorithm 4: Equal Cells Audit(F, Equal Cells Counter(F)) ... Algorithm 5: AFCounter(F) ... Algorithm 6: Count Auditor(F, AFCounter(F))
Open Source Code No The paper does not provide an explicit statement about releasing the source code for the methodology described, nor does it provide a direct link to a code repository for their implementation.
Open Datasets No The paper is theoretical and primarily deals with Boolean formulas in a general sense, without specifying any particular dataset or providing access information for one.
Dataset Splits No This paper is theoretical and does not involve empirical validation on datasets, therefore, it does not specify training, validation, or test dataset splits.
Hardware Specification No The paper is theoretical and does not describe any experimental setup that would require hardware specifications.
Software Dependencies No The paper discusses the theoretical use of SAT/QBF solvers as proxies for oracles but does not specify any particular software, libraries, or their version numbers necessary for reproducing results.
Experiment Setup No The paper is theoretical and focuses on algorithm design and complexity analysis, not empirical experiments. Therefore, it does not include details on experimental setup, hyperparameters, or training configurations.