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

TL;DR

This paper introduces a framework for generating step-by-step explanations of how to solve constraint satisfaction problems, aiming to make the inference process more interpretable and trustworthy for humans, especially in logic puzzles.

Contribution

It proposes a cost-based method for producing human-friendly explanations of inference steps in CSP solving, regardless of the underlying propagation mechanisms.

Findings

The approach produces explanations that are easy for humans to verify.

The explanation sequences are of high quality and interpretable.

The method is effective on logic grid puzzles.

Abstract

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.