Complete FSM Testing Using Strong Separability

• URL: http://arxiv.org/abs/2502.04035v1
• Date: Thu, 06 Feb 2025 12:54:24 GMT
• Title: Complete FSM Testing Using Strong Separability
• Abstract summary: Apartness is a concept developed in constructive mathematics, which has resurfaced as a powerful notion for separating states.<n>We identify some fundamental shortcomings of apartness in quantitative models, such as in hybrid and systems.<n>We propose a closely-related alternative, called strong separability, and show that using it to replace apartness addresses the identified shortcomings.
• Score: 1.0128808054306186
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Apartness is a concept developed in constructive mathematics, which has resurfaced as a powerful notion for separating states in the area of model learning and model-based testing. We identify some fundamental shortcomings of apartness in quantitative models, such as in hybrid and stochastic systems. We propose a closely-related alternative, called strong separability and show that using it to replace apartness addresses the identified shortcomings. We adapt a well-known complete model-based testing method, called the Harmonized State Identifiers (HSI) method, to adopt the proposed notion of strong separability. We prove that the adapted HSI method is complete. As far as we are aware, this is the first work to show how complete test suites can be generated for quantitative models such as those found in the development of cyber-physical systems.

Related papers

• Learning Markov Processes as Sum-of-Square Forms for Analytical Belief Propagation [10.143540866021542]
  This paper proposes a functional modeling framework leveraging sparse Sum-of-Squares (SoS) forms for valid (conditional) density estimation.<n>We study the theoretical restrictions of modeling conditional densities using the SoS form, and propose a novel functional form for addressing such limitations.<n>Our results show that the proposed method achieves accuracy comparable to state-of-the-art approaches while requiring significantly less memory in low-dimensional spaces.
  arXiv  Detail & Related papers  (2026-04-08T19:01:18Z)
• SphUnc: Hyperspherical Uncertainty Decomposition and Causal Identification via Information Geometry [7.816699755198432]
  We introduce SphUnc, a unified framework combining hyperspherical representation learning with structural causal modeling.<n>A structural causal model on spherical latents enables directed influence identification and interventional reasoning via sample-based simulation.<n> Empirical evaluations on social and affective benchmarks demonstrate improved accuracy, better calibration, and interpretable causal signals.
  arXiv  Detail & Related papers  (2026-03-01T16:11:49Z)
• Quantifying and Attributing Submodel Uncertainty in Stochastic Simulation Models and Digital Twins [0.1234398109349733]
  This paper investigates how submodel uncertainty affects the estimation of system performance metrics.<n>We develop a framework for quantifying submodel uncertainty in simulation models and extend the framework to digital-twin settings.
  arXiv  Detail & Related papers  (2026-02-18T00:06:39Z)
• Matrix Factorization Framework for Community Detection under the Degree-Corrected Block Model [48.989531198582704]
  We show that DCBM inference can be reformulated as a constrained nonnegative matrix factorization problem.<n>Our approach is to any specific network structure and applies to graphs with any structure representable by a DCBM.<n> Experiments on synthetic and real benchmark networks show that our method detects communities comparable to those found by DCBM inference.
  arXiv  Detail & Related papers  (2026-01-09T19:16:29Z)
• System-Embedded Diffusion Bridge Models [12.103113408680587]
  We introduce System embedded Diffusion Bridge Models (SDBs)<n>SDBs embed the known linear measurement system into the coefficients of a generalization matrix-valued SDE.<n>This principled integration yields consistent improvements across diverse linear inverse problems.
  arXiv  Detail & Related papers  (2025-06-30T10:58:49Z)
• Model-free Methods for Event History Analysis and Efficient Adjustment (PhD Thesis) [55.2480439325792]
  This thesis is a series of independent contributions to statistics unified by a model-free perspective. The first chapter elaborates on how a model-free perspective can be used to formulate flexible methods that leverage prediction techniques from machine learning. The second chapter studies the concept of local independence, which describes whether the evolution of one process is directly influenced by another.
  arXiv  Detail & Related papers  (2025-02-11T19:24:09Z)
• Causality can systematically address the monsters under the bench(marks) [64.36592889550431]
  Benchmarks are plagued by various biases, artifacts, or leakage. Models may behave unreliably due to poorly explored failure modes. causality offers an ideal framework to systematically address these challenges.
  arXiv  Detail & Related papers  (2025-02-07T17:01:37Z)
• Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference [47.460898983429374]
  We introduce an ensemble Kalman filter (EnKF) into the non-mean-field (NMF) variational inference framework to approximate the posterior distribution of the latent states. This novel marriage between EnKF and GPSSM not only eliminates the need for extensive parameterization in learning variational distributions, but also enables an interpretable, closed-form approximation of the evidence lower bound (ELBO) We demonstrate that the resulting EnKF-aided online algorithm embodies a principled objective function by ensuring data-fitting accuracy while incorporating model regularizations to mitigate overfitting.
  arXiv  Detail & Related papers  (2023-12-10T15:22:30Z)
• Distributionally Robust Model-based Reinforcement Learning with Large State Spaces [55.14361269378122]
  Three major challenges in reinforcement learning are the complex dynamical systems with large state spaces, the costly data acquisition processes, and the deviation of real-world dynamics from the training environment deployment. We study distributionally robust Markov decision processes with continuous state spaces under the widely used Kullback-Leibler, chi-square, and total variation uncertainty sets. We propose a model-based approach that utilizes Gaussian Processes and the maximum variance reduction algorithm to efficiently learn multi-output nominal transition dynamics.
  arXiv  Detail & Related papers  (2023-09-05T13:42:11Z)
• Measuring and Modeling Uncertainty Degree for Monocular Depth Estimation [50.920911532133154]
  The intrinsic ill-posedness and ordinal-sensitive nature of monocular depth estimation (MDE) models pose major challenges to the estimation of uncertainty degree. We propose to model the uncertainty of MDE models from the perspective of the inherent probability distributions. By simply introducing additional training regularization terms, our model, with surprisingly simple formations and without requiring extra modules or multiple inferences, can provide uncertainty estimations with state-of-the-art reliability.
  arXiv  Detail & Related papers  (2023-07-19T12:11:15Z)
• Class-Incremental Mixture of Gaussians for Deep Continual Learning [15.49323098362628]
  We propose end-to-end incorporation of the mixture of Gaussians model into the continual learning framework. We show that our model can effectively learn in memory-free scenarios with fixed extractors.
  arXiv  Detail & Related papers  (2023-07-09T04:33:19Z)
• Distributed Bayesian Learning of Dynamic States [65.7870637855531]
  The proposed algorithm is a distributed Bayesian filtering task for finite-state hidden Markov models. It can be used for sequential state estimation, as well as for modeling opinion formation over social networks under dynamic environments.
  arXiv  Detail & Related papers  (2022-12-05T19:40:17Z)
• Bayesian Evidential Learning for Few-Shot Classification [22.46281648187903]
  Few-Shot Classification aims to generalize from base classes to novel classes given very limited labeled samples. State-of-the-art solutions involve learning to find a good metric and representation space to compute the distance between samples. Despite the promising accuracy performance, how to model uncertainty for metric-based FSC methods effectively is still a challenge.
  arXiv  Detail & Related papers  (2022-07-19T03:58:00Z)
• Model-Agnostic Few-Shot Open-Set Recognition [36.97433312193586]
  We tackle the Few-Shot Open-Set Recognition (FSOSR) problem. We focus on developing model-agnostic inference methods that can be plugged into any existing model. We introduce an Open Set Transductive Information Maximization method OSTIM.
  arXiv  Detail & Related papers  (2022-06-18T16:27:59Z)
• FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation [69.34011200590817]
  We introduce FiLM-Ensemble, a deep, implicit ensemble method based on the concept of Feature-wise Linear Modulation. By modulating the network activations of a single deep network with FiLM, one obtains a model ensemble with high diversity. We show that FiLM-Ensemble outperforms other implicit ensemble methods, and it comes very close to the upper bound of an explicit ensemble of networks.
  arXiv  Detail & Related papers  (2022-05-31T18:33:15Z)
• Parsimony-Enhanced Sparse Bayesian Learning for Robust Discovery of Partial Differential Equations [5.584060970507507]
  A Parsimony Enhanced Sparse Bayesian Learning (PeSBL) method is developed for discovering the governing Partial Differential Equations (PDEs) of nonlinear dynamical systems. Results of numerical case studies indicate that the governing PDEs of many canonical dynamical systems can be correctly identified using the proposed PeSBL method.
  arXiv  Detail & Related papers  (2021-07-08T00:56:11Z)
• Identification of Probability weighted ARX models with arbitrary domains [75.91002178647165]
  PieceWise Affine models guarantees universal approximation, local linearity and equivalence to other classes of hybrid system. In this work, we focus on the identification of PieceWise Auto Regressive with eXogenous input models with arbitrary regions (NPWARX) The architecture is conceived following the Mixture of Expert concept, developed within the machine learning field.
  arXiv  Detail & Related papers  (2020-09-29T12:50:33Z)
• A Bootstrap-based Method for Testing Network Similarity [0.0]
  This paper studies the matched network inference problem. The goal is to determine if two networks, defined on a common set of nodes, exhibit a specific form of similarity. Two notions of similarity are considered: (i) equality, i.e., testing whether the networks arise from the same random graph model, and (ii) scaling, i.e., testing whether their probability are proportional for some unknown scaling constant.
  arXiv  Detail & Related papers  (2019-11-15T20:50:22Z)

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.