Hierarchical Causal Abduction: A Foundation Framework for Explainable Model Predictive Control

• URL: http://arxiv.org/abs/2605.10624v1
• Date: Mon, 11 May 2026 14:16:39 GMT
• Title: Hierarchical Causal Abduction: A Foundation Framework for Explainable Model Predictive Control
• Abstract summary: nonlinear dynamics, hard safety constraints, and numerical optimization often render individual control moves opaque to human operators.<n>This paper presents Hierarchical Causal Abduction (HCA), which combines (i) physics-informed reasoning via domain knowledge graphs, (ii) optimization evidence from Karush--Kuhn--Tucker (KKT) multipliers, and (iii) temporal causal discovery via the PCMCI algorithm to generate faithful, human-interpretable explanations for control actions computed by nonlinear MPC.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model Predictive Control (MPC) is widely used to operate safety-critical infrastructure by predicting future trajectories and optimizing control actions. However, nonlinear dynamics, hard safety constraints, and numerical optimization often render individual control moves opaque to human operators, undermining trust and hindering deployment. This paper presents Hierarchical Causal Abduction (HCA), which combines (i) physics-informed reasoning via domain knowledge graphs, (ii) optimization evidence from Karush--Kuhn--Tucker (KKT) multipliers, and (iii) temporal causal discovery via the PCMCI algorithm to generate faithful, human-interpretable explanations for control actions computed by nonlinear MPC. Across three diverse control applications (greenhouse climate, building HVAC, chemical process engineering) with expert validation, HCA improves explanation accuracy by 53\% over LIME (0.478 vs. 0.311) using a single set of cross-domain parameters without per-domain tuning; domain-specific KKT-threshold calibration over 2--3 days further increases accuracy to 0.88. Ablation studies confirm that each evidence source is essential, with 32--37\% accuracy degradation when any component is removed, and HCA's ranking-and-validation methodology generalizes beyond MPC to other prediction-based decision systems, including learning-based control and trajectory planning.

Related papers

• Behavior Cloning of MPC for 3-DOF Robotic Manipulators [0.0]
  Model Predictive Control (MPC) provides strong stability and robustness, but it imposes a significant computational burden on real-time systems.<n>This paper investigates the application of Behavior Cloning to approximate MPC policies for the real-time control of a 3-degree-of-freedom robotic manipulator.
  arXiv  Detail & Related papers  (2026-05-29T21:51:43Z)
• TabCausal: Pretraining Across Causal Environments for Tabular Causal Discovery [47.81267771630766]
  Causal discovery foundation models (CDFMs) seek to amortize this problem by mapping a dataset directly to a causal graph in a single forward pass.<n>We propose TabCausal, a data-driven CDFM trained with broad causal pretraining over diverse graph priors.
  arXiv  Detail & Related papers  (2026-05-29T11:05:43Z)
• Control Physiology: An Agent-Based Model of FAIR-CAM Dynamics [0.14323566945483493]
  We present the first agent-based model to operationalize the core FAIR-CAM dynamics.<n>In a hospital ransomware scenario, three organizational dynamics emerge that static analysis cannot represent.<n>These dynamics are structural properties of the FAIR-CAM architecture and should generalize beyond the specific scenario studied.
  arXiv  Detail & Related papers  (2026-05-26T06:31:20Z)
• Cognitive Friction: A Decision-Theoretic Framework for Bounded Deliberation in Tool-Using Agents [0.0]
  Unconstrained tool-using agents must decide which information source to query and act on.<n>We propose the Triadic Cognitive Architecture (TCA), a decision-theoretic framework that formalizes these failure modes via cognitive friction.<n>We validate TCA in two controlled environments designed to isolate stopping quality, action selection under congestion, and temporal urgency.
  arXiv  Detail & Related papers  (2026-03-31T17:30:25Z)
• CausalWrap: Model-Agnostic Causal Constraint Wrappers for Tabular Synthetic Data [4.08271266107383]
  CausalWrap is a model-agnostic wrapper that injects partial causal knowledge into any pretrained base generator.<n>CW learns a lightweight, differentiable post-hoc correction map applied to samples from the base generator.<n>CW improves causal fidelity across diverse base generators.
  arXiv  Detail & Related papers  (2026-03-02T15:59:46Z)
• Towards a Science of Scaling Agent Systems [79.64446272302287]
  We formalize a definition for agent evaluation and characterize scaling laws as the interplay between agent quantity, coordination structure, modelic, and task properties.<n>We derive a predictive model using coordination metrics, that cross-validated R2=0, enabling prediction on unseen task domains.<n>We identify three effects: (1) a tool-coordination trade-off: under fixed computational budgets, tool-heavy tasks suffer disproportionately from multi-agent overhead, and (2) a capability saturation: coordination yields diminishing or negative returns once single-agent baselines exceed 45%.
  arXiv  Detail & Related papers  (2025-12-09T06:52:21Z)
• Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning [70.56067503630486]
  We argue that sixth-generation (6G) intelligence is not fluent token prediction but calibrated the capacity to imagine and choose.<n>We show that WM-MS3M cuts mean absolute error (MAE) by 1.69% versus MS3M with 32% fewer parameters and similar latency, and achieves 35-80% lower root mean squared error (RMSE) than attention/hybrid baselines with 2.3-4.1x faster inference.
  arXiv  Detail & Related papers  (2025-11-04T17:22:22Z)
• Continual Action Quality Assessment via Adaptive Manifold-Aligned Graph Regularization [53.82400605816587]
  Action Quality Assessment (AQA) quantifies human actions in videos, supporting applications in sports scoring, rehabilitation, and skill evaluation.<n>A major challenge lies in the non-stationary nature of quality distributions in real-world scenarios.<n>We introduce Continual AQA (CAQA), which equips with Continual Learning capabilities to handle evolving distributions.
  arXiv  Detail & Related papers  (2025-10-08T10:09:47Z)
• Composable Score-based Graph Diffusion Model for Multi-Conditional Molecular Generation [85.58520120011269]
  We propose Composable Score-based Graph Diffusion model (CSGD), which extends score matching to discrete graphs via concrete scores.<n>We show that CSGD achieves state-of-the-art performance with a 15.3% average improvement in controllability over prior methods.<n>Our findings highlight the practical advantages of score-based modeling for discrete graph generation and its capacity for flexible, multi-property molecular design.
  arXiv  Detail & Related papers  (2025-09-11T13:37:56Z)
• Technical Report: Facilitating the Adoption of Causal Inference Methods Through LLM-Empowered Co-Pilot [44.336297829718795]
  We introduce CATE-B, an open-source co-pilot system that uses large language models (LLMs) within an agentic framework to guide users through treatment effect estimation.<n>CATE-B assists in (i) constructing a structural causal model via causal discovery and LLM-based edge orientation, (ii) identifying robust adjustment sets through a novel Minimal Uncertainty Adjustment Set criterion, and (iii) selecting appropriate regression methods tailored to the causal structure and dataset characteristics.
  arXiv  Detail & Related papers  (2025-08-14T12:20:51Z)
• Evaluating Ensemble and Deep Learning Models for Static Malware Detection with Dimensionality Reduction Using the EMBER Dataset [0.0]
  This study investigates the effectiveness of several machine learning algorithms for static malware detection using the EMBER dataset.<n>We evaluate eight classification models: LightGBM, XGBoost, CatBoost, Random Forest, Extra Trees, HistGradientBoosting, k-Nearest Neighbors (KNN), and TabNet.<n>The models are assessed on accuracy, precision, recall, F1 score, and AUC to examine both predictive performance and robustness.
  arXiv  Detail & Related papers  (2025-07-22T18:45:10Z)
• Bisimulation metric for Model Predictive Control [44.301098448479195]
  Bisimulation Metric for Model Predictive Control (BS-MPC) is a novel approach that incorporates bisimulation metric loss in its objective function to directly optimize the encoder. BS-MPC improves training stability, robustness against input noise, and computational efficiency by reducing training time. We evaluate BS-MPC on both continuous control and image-based tasks from the DeepMind Control Suite.
  arXiv  Detail & Related papers  (2024-10-06T17:12:10Z)
• Decoupling of neural network calibration measures [45.70855737027571]
  We investigate the coupling of different neural network calibration measures with a special focus on the Area Under Sparsification Error curve (AUSE) metric. We conclude that the current methodologies leave a degree of freedom, which prevents a unique model for the homologation of safety-critical functionalities.
  arXiv  Detail & Related papers  (2024-06-04T15:21:37Z)
• End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear Model Predictive Control [45.84205238554709]
  We present a method for reinforcement learning of Koopman surrogate models for optimal performance as part of (e)NMPC. We show that the end-to-end trained models outperform those trained using system identification in (e)NMPC.
  arXiv  Detail & Related papers  (2023-08-03T10:21:53Z)

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.