10 accepted papers
Hybrid tables, also referred to as `smart' in the literature, represent a valuable modeling technique within Constraint Programming (CP). These tables allow us to handle disjunctive cases (constraining expressions) in a compact and structured way, by authorizing tuples (table entries) to contain sim
Coverage-based prime implicant explanations are formal explanations offering a number of valuable assets, especially in terms of faithfulness and generality. Unfortunately, deriving a coverage-based prime implicant explanation for an instance is computationally hard in the general case (the problem
More than ever, air transport players (i.e., airline and airport companies) in an intensely competitive climate need to benefit from a carefully optimized management of airport resources to improve the quality of service and control the induced costs. In this paper, we investigate the Airport Check-…
We are interested in identifying the complexity of computing local explanations of various types given a decision tree, when the Boolean conditions used in the tree are not independent. This is usually the case when decision trees are learned from instances described using numerical or categorical a…
We show how to define and compute example-based abductive explanations. Such explanations are guaranteed to be 100% correct, fairly general, and persuasive enough since they cover sufficiently many reference instances furnished by the explainee. We prove that the latter coverage condition yields a c…
PyXAI (Python eXplainable AI) is a Python library designed for providing explanations and cor- recting tree-based Machine Learning (ML) models. It is suited to decision trees, random forests, and boosted trees, when used for regression or classification tasks. In contrast to many model-agnostic appr…
We present two algorithms for generating (resp. evaluating) abductive explanations for boosted regression trees. Given an instance x and an interval I containing its value F (x) for the boosted regression tree F at hand, the generation algorithm returns a (most general) term t over the Boolean condi…
Boosted trees is a dominant ML model, exhibiting high accuracy. However, boosted trees are hardly intelligible, and this is a problem whenever they are used in safety-critical applications. Indeed, in such a context, provably sound explanations for the predictions made are expected. Recent work have…
Abductive explanations take a central place in eXplainable Artificial Intelligence (XAI) by clarifying with few features the way data instances are classified. However, instances may have exponentially many minimum-size abductive explanations, and this source of complexity holds even for ``intell…
Random forests have long been considered as powerful model ensembles in machine learning. By training multiple decision trees, whose diversity is fostered through data and feature subsampling, the resulting random forest can lead to more stable and reliable predictions than a single decision tree. T…