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dtControl 2.0: Explainable Strategy Representation via Decision Tree Learning Steered by Experts

arXiv 2101.07202 ↗First posted 15 Jan 2021cs.AI cs.FL cs.LGcited by 14

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Abstract (arXiv)

Recent advances have shown how decision trees are apt data structures for concisely representing strategies (or controllers) satisfying various objectives. Moreover, they also make the strategy more explainable. The recent tool dtControl had provided pipelines with tools supporting strategy synthesis for hybrid systems, such as SCOTS and Uppaal Stratego. We present dtControl 2.0, a new version with several fundamentally novel features. Most importantly, the user can now provide domain knowledge to be exploited in the decision tree learning process and can also interactively steer the process based on the dynamically provided information. To this end, we also provide a graphical user interface. It allows for inspection and re-computation of parts of the result, suggesting as well as receiving advice on predicates, and visual simulation of the decision-making process. Besides, we interface model checkers of probabilistic systems, namely Storm and PRISM and provide dedicated support for categorical enumeration-type state variables. Consequently, the controllers are more explainable and smaller.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 10 of its 48 references that are in the map. First posted on arXiv 15 Jan 2021.

Builds on

10 of 48 references.

References · 10

  1. 2017A storm is Coming: A Modern Probabilistic Model Checkercited by 34
  2. 2020dtControl: Decision Tree Learning Algorithms for Controller Representationcited by 12
  3. 2015Counterexample Explanation by Learning Small Strategies in Markov Decision Processescited by 10
  4. 2019SOS: Safe, Optimal and Small Strategies for Hybrid Markov Decision Processescited by 9
  5. 2013SPUDD: Stochastic Planning using Decision Diagramscited by 7
  6. 2018Strategy Representation by Decision Trees in Reactive Synthesiscited by 5
  7. 2019Practical Synthesis of Reactive Systems from LTL Specifications via Parity Gamescited by 5
  8. 2019Strategy Representation by Decision Trees with Linear Classifierscited by 5
  9. 2018Optimal Symbolic Controllers Determinization for BDD storagecited by 4
  10. 2019Learning-Based Synthesis of Safety Controllerscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 4; 2025: 4; 2026: 5.

14 papers.

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  1. 2022Algebraically Explainable Controllers: Decision Trees and Support Vector Machines Join Forcescited by 5
  2. 20241-2-3-Go! Policy Synthesis for Parameterized Markov Decision Processes via Decision-Tree Learning and Generalizationcited by 5
  3. 2024Learning Explainable and Better Performing Representations of POMDP Strategiescited by 4
  4. 2026Formally Verifying and Explaining Sepsis Treatment Policies with COOL-MCcited by 3
  5. 2025Constrained and Robust Policy Synthesis with Satisfiability-Modulo-Probabilistic-Model-Checkingcited by 2
  6. 2024In Search of Trees: Decision-Tree Policy Synthesis for Black-Box Systems via Searchcited by 1
  7. 2025Taming Infinity one Chunk at a Time: Concisely Represented Strategies in One-Counter MDPscited by 1
  8. 2025Explaining Control Policies through Predicate Decision Diagramscited by 1
  9. 2025Uppaal Coshy: Automatic Synthesis of Compact Shields for Hybrid Systemscited by 1
  10. 2024Explainable Representation of Finite-Memory Policies for POMDPs using Decision Treescited by 0
  11. 2026COOL-MC: Verifying and Explaining RL Policies for Platelet Inventory Managementcited by 0
  12. 2026Explainably Safe Reinforcement Learningcited by 0
2 more
  1. 2026dtControl2+varepsilon: Trading Optimality for Explainability in MDPs via Decision Treescited by 0
  2. 2026Automated Testing of LLM-Based Post Hoc Explainers Using Model Checking as an Oraclecited by 0