BlackBoxToBlueprint: Extracting Interpretable Logic from Legacy Systems using Reinforcement Learning and Counterfactual Analysis

• URL: http://arxiv.org/abs/2507.00180v1
• Date: Mon, 30 Jun 2025 18:36:54 GMT
• Title: BlackBoxToBlueprint: Extracting Interpretable Logic from Legacy Systems using Reinforcement Learning and Counterfactual Analysis
• Authors: Vidhi Rathore,
• Abstract summary: This paper proposes a novel pipeline to automatically extract interpretable decision logic from legacy systems treated as black boxes.<n>The approach uses a Reinforcement Learning (RL) agent to explore the input space and identify critical decision boundaries by rewarding actions that cause meaningful changes in the system's output.<n>Decision trees are then trained on these clusters to extract human-readable rules that approximate the system's decision logic near the identified boundaries.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modernizing legacy software systems is a critical but challenging task, often hampered by a lack of documentation and understanding of the original system's intricate decision logic. Traditional approaches like behavioral cloning merely replicate input-output behavior without capturing the underlying intent. This paper proposes a novel pipeline to automatically extract interpretable decision logic from legacy systems treated as black boxes. The approach uses a Reinforcement Learning (RL) agent to explore the input space and identify critical decision boundaries by rewarding actions that cause meaningful changes in the system's output. These counterfactual state transitions, where the output changes, are collected and clustered using K-Means. Decision trees are then trained on these clusters to extract human-readable rules that approximate the system's decision logic near the identified boundaries. I demonstrated the pipeline's effectiveness on three dummy legacy systems with varying complexity, including threshold-based, combined-conditional, and non-linear range logic. Results show that the RL agent successfully focuses exploration on relevant boundary regions, and the extracted rules accurately reflect the core logic of the underlying dummy systems, providing a promising foundation for generating specifications and test cases during legacy migration.

Related papers

• Multi-modal Causal Structure Learning and Root Cause Analysis [67.67578590390907]
  We propose Mulan, a unified multi-modal causal structure learning method for root cause localization. We leverage a log-tailored language model to facilitate log representation learning, converting log sequences into time-series data. We also introduce a novel key performance indicator-aware attention mechanism for assessing modality reliability and co-learning a final causal graph.
  arXiv  Detail & Related papers  (2024-02-04T05:50:38Z)
• Logic-induced Diagnostic Reasoning for Semi-supervised Semantic Segmentation [85.12429517510311]
  LogicDiag is a neural-logic semi-supervised learning framework for semantic segmentation. Our key insight is that conflicts within pseudo labels, identified through symbolic knowledge, can serve as strong yet commonly ignored learning signals. We showcase the practical application of LogicDiag in the data-hungry segmentation scenario, where we formalize the structured abstraction of semantic concepts as a set of logic rules.
  arXiv  Detail & Related papers  (2023-08-24T06:50:07Z)
• A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systems [0.0]
  We study the different types of feedback loops in the ML-based decision-making pipeline. We find that the existence of feedback loops can perpetuate, reinforce, or even reduce ML biases.
  arXiv  Detail & Related papers  (2023-05-10T11:15:22Z)
• Interactive System-wise Anomaly Detection [66.3766756452743]
  Anomaly detection plays a fundamental role in various applications. It is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data. We develop an end-to-end approach which includes an encoder-decoder module that learns system embeddings.
  arXiv  Detail & Related papers  (2023-04-21T02:20:24Z)
• Dealing with Collinearity in Large-Scale Linear System Identification Using Gaussian Regression [3.04585143845864]
  We consider estimation of networks consisting of several interconnected dynamic systems. We develop a strategy cast in a Bayesian regularization framework where any impulse response is seen as realization of a zero-mean Gaussian process. We design a novel Markov chain Monte Carlo scheme able to reconstruct the impulse responses posterior by efficiently dealing with collinearity.
  arXiv  Detail & Related papers  (2023-02-21T19:35:47Z)
• Large-Scale Sequential Learning for Recommender and Engineering Systems [91.3755431537592]
  In this thesis, we focus on the design of an automatic algorithms that provide personalized ranking by adapting to the current conditions. For the former, we propose novel algorithm called SAROS that take into account both kinds of feedback for learning over the sequence of interactions. The proposed idea of taking into account the neighbour lines shows statistically significant results in comparison with the initial approach for faults detection in power grid.
  arXiv  Detail & Related papers  (2022-05-13T21:09:41Z)
• Formal Verification of Unknown Dynamical Systems via Gaussian Process Regression [11.729744197698718]
  Leveraging autonomous systems in safety-critical scenarios requires verifying their behaviors in the presence of uncertainties. We develop a framework for verifying discrete-time dynamical systems with unmodelled dynamics and noisy measurements.
  arXiv  Detail & Related papers  (2021-12-31T05:10:05Z)
• Logical blocks for fault-tolerant topological quantum computation [55.41644538483948]
  We present a framework for universal fault-tolerant logic motivated by the need for platform-independent logical gate definitions. We explore novel schemes for universal logic that improve resource overheads. Motivated by the favorable logical error rates for boundaryless computation, we introduce a novel computational scheme.
  arXiv  Detail & Related papers  (2021-12-22T19:00:03Z)
• A new interpretable unsupervised anomaly detection method based on residual explanation [47.187609203210705]
  We present RXP, a new interpretability method to deal with the limitations for AE-based AD in large-scale systems. It stands out for its implementation simplicity, low computational cost and deterministic behavior. In an experiment using data from a real heavy-haul railway line, the proposed method achieved superior performance compared to SHAP.
  arXiv  Detail & Related papers  (2021-03-14T15:35:45Z)
• Active Learning for Nonlinear System Identification with Guarantees [102.43355665393067]
  We study a class of nonlinear dynamical systems whose state transitions depend linearly on a known feature embedding of state-action pairs. We propose an active learning approach that achieves this by repeating three steps: trajectory planning, trajectory tracking, and re-estimation of the system from all available data. We show that our method estimates nonlinear dynamical systems at a parametric rate, similar to the statistical rate of standard linear regression.
  arXiv  Detail & Related papers  (2020-06-18T04:54:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.