1,721,121 research outputs found
Reason to Explain: Interactive Contrastive Explanations (REASONX)
No full textMany high-performing machine learning models are not interpretable. As they are increasingly used in decision scenarios that can critically affect individuals, it is necessary to develop tools to better understand their outputs. Popular explanation methods include contrastive explanations. However, they suffer several shortcomings, among others an insufficient incorporation of background knowledge, and a lack of interactivity. While (dialogue-like) interactivity is important to better communicate an explanation, background knowledge has the potential to significantly improve their quality, e.g., by adapting the explanation to the needs of the end-user. To close this gap, we present reasonx, an explanation tool based on Constraint Logic Programming (CLP). reasonx provides interactive contrastive explanations that can be augmented by background knowledge, and allows to operate under a setting of under-specified information, leading to increased flexibility in the provided explanations. reasonx computes factual and contrastive decision rules, as well as closest contrastive examples. It provides explanations for decision trees, which can be the ML models under analysis, or global/local surrogate models of any ML model. While the core part of reasonx is built on CLP, we also provide a program layer that allows to compute the explanations via Python, making the tool accessible to a wider audience. We illustrate the capability of reasonx on a synthetic data set, and on a well-developed example in the credit domain. In both cases, we can show how reasonx can be flexibly used and tailored to the needs of the user
Declarative Reasoning on Explanations Using Constraint Logic Programming
No full textExplaining opaque Machine Learning (ML) models is an increasingly relevant problem. Current explanation in AI (XAI) methods suffer several shortcomings, among others an insufficient incorporation of background knowledge, and a lack of abstraction and interactivity with the user. We propose reasonx, an explanation method based on Constraint Logic Programming (CLP). reasonx can provide declarative, interactive explanations for decision trees, which can be the ML models under analysis or global/local surrogate models of any black-box model. Users can express background or common sense knowledge using linear constraints and MILP optimization over features of factual and contrastive instances, and interact with the answer constraints at different levels of abstraction through constraint projection. We present here the architecture of reasonx, which consists of a Python layer, closer to the user, and a CLP layer. reasonx’s core execution engine is a Prolog meta-program with declarative semantics in terms of logic theories
Qualitative Spatial Reasoning in a Logical Framework
No full textIn this paper we present an approach to qualitative spatial reasoning based on the spatio-temporal language STACLP 18. In particular, we show how the topological 9-intersection model 7 and the direction relations based on projections 16 can be modelled in such a framework. STACLP is a constraint logic programming language where formulae can be annotated with labels (annotations) and where relations between these labels can be expressed by using constraints. Annotations are used to represent both time and space
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