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Found 7 papers, 1 papers with code
Tackling Universal Properties of Minimal Trap Spaces of Boolean Networks
In this paper, we address the logical reasoning on universal properties of MTSs in the scope of two problems: the reprogramming of Boolean networks for identifying the permanent freeze of Boolean variables that enforce a given property on all the MTSs, and the synthesis of Boolean networks from universal properties on their MTSs.
Computing Abductive Explanations for Boosted Trees
However, the generation of such well-founded explanations is intractable in the general case.
Design and Results of ICCMA 2021
This paper discusses the design of the Fourth International Competition on Computational Models of Argumentation.
Trading Complexity for Sparsity in Random Forest Explanations
Notably, as an alternative to sufficient reasons taking the form of prime implicants of the random forest, we introduce majoritary reasons which are prime implicants of a strict majority of decision trees.
On the Explanatory Power of Decision Trees
We finally show that, unlike sufficient reasons, the set of all contrastive explanations for an instance given a decision tree can be derived, minimized and counted in polynomial time.
On the Computational Intelligibility of Boolean Classifiers
In this paper, we investigate the computational intelligibility of Boolean classifiers, characterized by their ability to answer XAI queries in polynomial time.
Improving MUC extraction thanks to local search
ExtractingMUCs(MinimalUnsatisfiableCores)fromanunsatisfiable constraint network is a useful process when causes of unsatisfiability must be understood so that the network can be re-engineered and relaxed to become sat- isfiable.
