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Towards Step-Wise Explanations of Large Search Trees (Short Paper)

Authors Ignace Bleukx , Peter J. Stuckey , Tias Guns

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LIPIcs.CP.2026.62.pdf
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Subject Classification

ACM Subject Classification
  • Computing methodologies → Causal reasoning and diagnostics
  • Software and its engineering → Constraint and logic languages
Keywords
  • Explanation
  • search
  • propagation
  • proof

Metrics

Abstract

Modern constraint solvers solve combinatorial problems through search with branching, propagation, and nogood learning. Although effective, the resulting search trees are hard to interpret: many branches and low-level inferences obscure why a conclusion is reached. Step-wise explanations provide an inference-based alternative, but prior successes were mainly for puzzle-style problems that required little or no search when solved by a CP-solver. We investigate whether step-wise explanations can be extended to search-heavy combinatorial problems. We study explanation sequences with only user-level constraints, ideally just one per step, and construct them from solver proof logs through nested explanations of complex steps. Our results indicate that concise user-level explanations are often achievable, even when solving requires many search nodes, while also highlighting open challenges such as deep nesting in some instances and dependence on proof generation. This motivates future work on explanation-aware solving and richer explanation languages.

Cite As Get BibTex

Ignace Bleukx, Peter J. Stuckey, and Tias Guns. Towards Step-Wise Explanations of Large Search Trees (Short Paper). In 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 379, pp. 62:1-62:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.CP.2026.62

Author Details

Ignace Bleukx
  • Department of Computer Science, KU Leuven, Belgium
Peter J. Stuckey
  • Department of Data Science and AI, Monash University, Clayton, Australia
  • OPTIMA ARC Industrial Training and Transformation Centre, Melbourne, Australia
Tias Guns
  • Department of Computer Science, KU Leuven, Belgium

Funding

This research is partly funded by the European Research Council (ERC) under the EU Horizon 2020 research and innovation programme (Grant No 101002802, CHAT-Opt), by the Research Foundation-Flanders (FWO-Vlaanderen, G020525N) and by the Australian Government through the Australian Research Council Industrial Transformation Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications (OPTIMA).

Supplementary Materials

References

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