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Author: Guillaume Petiot

Affiliation: CERES, Catholic Institute of Toulouse, 31 Rue de la Fonderie, Toulouse, France

Keyword(s): Possibilistic Networks, Uncertainty, Knowledge Compiling, Possibility Theory, Inference.

Abstract: Compiling Possibilistic Networks consists in evaluating the effect of evidence by encoding the possibilistic network in the form of a multivariable function. This function can be factored and represented by a graph which allows the calculation of new conditional possibilities. Encoding the possibilistic network in Conjunctive Normal Form (CNF) makes it possible to factorize the multivariable function and generate a deterministic graph in Decomposable Negative Normal Form (d-DNNF) whose computation time is polynomial. The challenge of compiling possibilistic networks is to minimize the number of clauses and the size of the d-DNNF graph to guarantee the lowest possible computation time. Several solutions exist to reduce the number of CNF clauses. We present in this paper several improvements for the encoding of possibilistic networks. We will then focus our interest on the use of Quine-McCluskey’s algorithm (QMC) to simplify and reduce the number of clauses.

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Paper citation in several formats:
Petiot, G. (2023). Improved Encoding of Possibilistic Networks in CNF Using Quine-McCluskey Algorithm. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-623-1; ISSN 2184-433X, SciTePress, pages 798-805. DOI: 10.5220/0011777100003393

@conference{icaart23,
author={Guillaume Petiot.},
title={Improved Encoding of Possibilistic Networks in CNF Using Quine-McCluskey Algorithm},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2023},
pages={798-805},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011777100003393},
isbn={978-989-758-623-1},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Improved Encoding of Possibilistic Networks in CNF Using Quine-McCluskey Algorithm
SN - 978-989-758-623-1
IS - 2184-433X
AU - Petiot, G.
PY - 2023
SP - 798
EP - 805
DO - 10.5220/0011777100003393
PB - SciTePress

- Science and Technology Publications, Lda.
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