On Computational Aspects of Iterated Belief Change

Authors: Nicolas Schwind, Sebastien Konieczny, Jean-Marie Lagniez, Pierre Marquis

IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We also show that a revised belief state can be computed in a reasonable time for large-sized instances using SAT-based algorithms, and we report empirical results showing the feasibility of iterated belief change for bases of significant sizes.
Researcher Affiliation Collaboration Nicolas Schwind1 , S ebastien Konieczny2 , Jean-Marie Lagniez3 and Pierre Marquis2,4 1National Institute of Advanced Industrial Science and Technology, Tokyo, Japan 2CRIL CNRS, Universit e d Artois, Lens, France 3Huawei Technologies Ltd, Boulogne-Billancourt, France 4Institut Universitaire de France
Pseudocode Yes Proposition 5. Let Φ = (ϕ1, . . . , ϕn) and α, µ be two formulae. Then |= O can be decided by the following procedure: 1. if ϕ1 µ α |= then return False; 2. if ϕ1 µ |= then return True; 3. if (ϕ1 (ϕ2 µ)) α |= then return False; 4. Return True;
Open Source Code No Lastly, we will implement some other change operators (especially, those described in [Rott, 2009]) and make the code we developed for generating and querying CWRs available online.
Open Datasets No The paper describes generating synthetic instances ('3-CNF formulae') for experiments rather than using a pre-existing public dataset with standard training splits. No information about train sets is provided.
Dataset Splits No The paper describes generating synthetic instances for experiments and does not mention any validation dataset splits.
Hardware Specification Yes All the experiments have been conducted on a cluster of Intel Xeon E5-2643 (3.30 GHz) quad core processors with 32 Gi B RAM.
Software Dependencies No The SAT solver used in the experiments is Mini SAT [E en and S orensson, 2003]. However, no specific version number for Mini SAT or other ancillary software is provided.
Experiment Setup Yes The propositional formulae ϕ and µ considered in our experiments are 3-CNF formulae (i.e., conjunctions of clauses of size at most 3), built using the modularity-based random generator presented in [Gir aldez-Cru and Levy, 2015]. ... for each instance, the 3-CNF formula ϕ considered at start is built over 4048 variables, has 12144 clauses, and is obtained using a modularity of 512, while every change formula µ of the sequence is a 3-CNF formula generated from 814 variables (among the 4048 ones used in ϕ), with 2442 clauses and for a modularity picked up at random between 3 and 50. A time-out of 900s and a memory-out of 7.6 Gi B has been considered for each instance.