A framework for step-wise explaining how to solve constraint satisfaction problems

Bogaerts, Bart; Gamba, Emilio; Guns, Tias

doi:10.1016/j.artint.2021.103550

Cited by 22 publications

(52 citation statements)

References 27 publications

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“…Publication history. This paper is an extension of previous papers presented at workshops and conferences [13,14,15]. The current paper extends the previous papers with more detailed examples, additional experiments, as well as the formalization of what we call nested explanation sequences.…”

Section: Introductionmentioning

confidence: 55%

A framework for step-wise explaining how to solve constraint satisfaction problems

Bogaerts,

Gamba,

Guns

2020

Preprint

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We explore the problem of step-wise explaining how to solve constraint satisfaction problems, with a use case on logic grid puzzles. More specifically, we study the problem of explaining the inference steps that one can take during propagation, in a way that is easy to interpret for a person. Thereby, we aim to give the constraint solver explainable agency, which can help in building trust in the solver by being able to understand and even learn from the explanations. The main challenge is that of finding a sequence of simple explanations, where each explanation should aim to be as cognitively easy as possible for a human to verify and understand. This contrasts with the arbitrary combination of facts and constraints that the solver may use when propagating. We propose the use of a cost function to quantify how simple an individual explanation of an inference step is, and identify the explanation-production problem of finding the best sequence of explanations of a CSP. Our approach is agnostic of the underlying constraint propagation mechanisms, and can provide explanations even for inference steps resulting from combinations of constraints. In case multiple constraints are involved, we also develop a mechanism that allows to break the most difficult steps up and thus gives the user the ability to zoom in on specific parts of the explanation. Our proposed algorithm iteratively constructs the explanation sequence by using an optimistic estimate of the cost function to guide the search for the best explanation at each step. Our experiments on logic grid puzzles show the feasibility of the approach in terms of the quality of the individual explanations and the resulting explanation sequences obtained.

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Section: Introductionmentioning

confidence: 55%

A framework for step-wise explaining how to solve constraint satisfaction problems

Bogaerts,

Gamba,

Guns

2020

Preprint

Self Cite

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show abstract

“…Although recent years have witnessed a growing interest in finding explanations of machine learning (ML) models (Lipton 2018;Guidotti et al 2019;Weld and Bansal 2019;Monroe 2021), explanations have been studied from different perspectives and in different branches of AI at least since the 80s (Shanahan 1989;Falappa, Kern-Isberner, and Simari 2002;Pérez and Uzcátegui 2003), including more recently in constraint programming (Amilhastre, Fargier, and Marquis 2002;Bogaerts et al 2020;Gamba, Bogaerts, and Guns 2021). In the case of ML models, non-heuristic explanations have been studied in recent years (Shih, Choi, and Darwiche 2018;Ignatiev, Narodytska, and Marques-Silva 2019a;Shih, Choi, and Darwiche 2019;Narodytska et al 2019;Ignatiev, Narodytska, and Marques-Silva 2019b,c;Darwiche and Hirth 2020;Ignatiev et al 2020a;Ignatiev 2020;Audemard, Koriche, and Marquis 2020;Marques-Silva et al 2020;Barceló et al 2020;Ignatiev et al 2020b;Izza, Ignatiev, and Marques-Silva 2020;Wäldchen et al 2021;Izza and Marques-Silva 2021;Malfa et al 2021;Ignatiev and Marques-Silva 2021;Cooper and Marques-Silva 2021;Huang et al 2021;Audemard et al 2021;Marques-Silva and Ignatiev 2022;Ignatiev et al 2022;Shrotri et al 2022).…”

Section: Related Workmentioning

confidence: 99%

Tractable Explanations for d-DNNF Classifiers

Huang

Izza

Ignatiev

et al. 2022

AAAI

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Compilation into propositional languages finds a growing number of practical uses, including in constraint programming, diagnosis and machine learning (ML), among others. One concrete example is the use of propositional languages as classifiers, and one natural question is how to explain the predictions made. This paper shows that for classifiers represented with some of the best-known propositional languages, different kinds of explanations can be computed in polynomial time. These languages include deterministic decomposable negation normal form (d-DNNF), and so any propositional language that is strictly less succinct than d-DNNF. Furthermore, the paper describes optimizations, specific to Sentential Decision Diagrams (SDDs), which are shown to yield more efficient algorithms in practice.

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“…Although at present ML model explainability of ML models is the most studied theme in the general field of explainability, it is also the case that explainability has been studied in AI for decades [11-13, 96, 103, 104, 113, 250, 276, 278, 292, 293], with a renewed interest in recent years. For example, explanations have recently been studied in AI planning [65,94,95,111,142,194,288,289,291,302], constraint satisfaction and problem solving [54,99,115,135,289], among other examples [290]. Furthermore, there is some agreement that regulations like EU's General Data Protection Regulation (GDPR) [100] effectively impose the obligation of explanations for any sort of algorithmic decision making [129,188].…”

Section: Additional Topics and Extensionsmentioning

confidence: 99%

Logic-Based Explainability in Machine Learning

Marques-Silva¹

2022

Preprint

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The last decade witnessed an ever-increasing stream of successes in Machine Learning (ML). These successes offer clear evidence that ML is bound to become pervasive in a wide range of practical uses, including many that directly affect humans. Unfortunately, the operation of the most successful ML models is incomprehensible for human decision makers. As a result, the use of ML models, especially in highrisk and safety-critical settings is not without concern. In recent years, there have been efforts on devising approaches for explaining ML models. Most of these efforts have focused on so-called model-agnostic approaches. However, all model-agnostic and related approaches offer no guarantees of rigor, hence being referred to as non-formal. For example, such non-formal explanations can be consistent with different predictions, which renders them useless in practice. This paper overviews the ongoing research efforts on computing rigorous model-based explanations of ML models; these being referred to as formal explanations. These efforts encompass a variety of topics, that include the actual definitions of explanations, the characterization of the complexity of computing explanations, the currently best logical encodings for reasoning about different ML models, and also how to make explanations interpretable for human decision makers, among others.

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A framework for step-wise explaining how to solve constraint satisfaction problems

Cited by 22 publications

References 27 publications

A framework for step-wise explaining how to solve constraint satisfaction problems

A framework for step-wise explaining how to solve constraint satisfaction problems

Tractable Explanations for d-DNNF Classifiers

Logic-Based Explainability in Machine Learning

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