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Found 11 papers, 0 papers with code
On the Complexity of Enumerating Prime Implicants from Decision-DNNF Circuits
We consider the problem EnumIP of enumerating prime implicants of Boolean functions represented by decision decomposable negation normal form (dec-DNNF) circuits.
Computing Abductive Explanations for Boosted Trees
However, the generation of such well-founded explanations is intractable in the general case.
Rectifying Mono-Label Boolean Classifiers
We elaborate on the notion of rectification of a Boolean classifier $\Sigma$.
Pseudo Polynomial-Time Top-k Algorithms for d-DNNF Circuits
Under the same assumption, we present a pseudo polynomial-time algorithm that transforms $C$ into a d-DNNF circuit $C'$ satisfied exactly by the models of $C$ having a value among the top-$k$ ones.
On Quantifying Literals in Boolean Logic and Its Applications to Explainable AI
This leads to a refinement of quantified Boolean logic with literal quantification as its primitive.
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.
On Irrelevant Literals in Pseudo-Boolean Constraint Learning
Learning pseudo-Boolean (PB) constraints in PB solvers exploiting cutting planes based inference is not as well understood as clause learning in conflict-driven clause learning solvers.
On Weakening Strategies for PB Solvers
While none of them performs better than the others on all benchmarks, applying weakening on the conflict side has surprising good performance, whereas applying partial weakening and division on both the conflict and the reason sides provides the best results overall.
On the Complexity of Optimization Problems based on Compiled NNF Representations
When the set of feasible solutions under consideration is of combinatorial nature and described in an implicit way as a set of constraints, optimization is typically NP-hard.
