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Source note3 min read

Explainable Control Framework (XCF) based on Fuzzy Model-Agnostic Explanation and LLM Agent-Supported Interface

Source

arXiv

arxiv.org/abs/2606.25941v1

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Source

Plain-Language Brief

The paper introduces an Explainable Control Framework (XCF) for understanding complex controllers' behavior in various scenarios. It offers three main contributions: the XCF, a model-agnostic explanation method called Hierarchical Fuzzy Model-Agnostic Explanation for Control Systems (HFMAE-C), and a large language model agent-supported user interface. The HFMAE-C provides sample, local, domain, and universe level explanations using IF-THEN rules and salience values. Case studies show the effectiveness of the proposed method.

What the Document Says

  • The paper proposes an Explainable Control Framework (XCF).
  • The XCF provides model-agnostic explanations for controllers in closed-loop systems and can refine local explanations based on system response dynamics (optional).
  • A novel explanation method, Hierarchical Fuzzy Model-Agnostic Explanation for Control Systems (HFMAE-C), is presented.
  • The HFMAE-C uses a fuzzy logic system to approximate the controller's behavior and system dynamics.
  • The HFMAE-C provides sample, local, domain, and universe level explanations via IF-THEN rules.
  • The HFMAE-C reveals the controller's decision logic and salience values quantifying the contribution of system states to control actions.
  • A large language model agent-supported user interface is developed.

Key Concepts Mentioned

  • Explainable Control Framework (XCF)
  • Hierarchical Fuzzy Model-Agnostic Explanation for Control Systems (HFMAE-C)
  • Large Language Model Agent
  • Controller
  • Closed-loop system

Claims Made by the Document

  • The XCF can provide human-understandable insights into controller behavior.
  • The HFMAE-C provides sample, local, domain, and universe level explanations revealing the controller's decision logic and salience values.

Evidence Used

  • Increasing demand for precise and reliable control in complex scenarios has led to...
  • This complexity highlights the needs for explainable control that can provide human-understandable insights into controller behavior.
  • The novel contributions of this work are threefold:
  • The HFMAE-C employs a fuzzy logic system to approximate the controller's behavior and system dynamics, providing sample, local, domain and universe level explanations via IF-THEN rules revealing the controller's decision logic and salience values quantifying the contribution of system states to control actions.

Methods / Approach

  • Explainable Control Framework (XCF)
  • Hierarchical Fuzzy Model-Agnostic Explanation for Control Systems (HFMAE-C)
  • Large Language Model Agent

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Increasing demand for precise and reliable control in complex scenarios has led to the development of increasingly sophisticated controllers, including data-driven approaches employing closed box models and mathematically rigorous yet complex designs. This complexity highlights the needs for explainable control that can provide human-understandable insights into controller behavior. In this paper, an explainable control framework (XCF) along with supporting algorithms and user interface are proposed to explain how controllers determine their control actions and their underlying working mechanism. The novel contributions of this work are threefold: First, the XCF is designed to provide model-agnostic explanations for controllers in closed-loop systems and can optionally refine local explanations by system response dynamics. Second, a novel explanation method, hierarchical fuzzy model-agnostic explanation for control systems (HFMAE-C), is proposed based on the designed framework. The HFMAE-C employs a fuzzy logic system to approximate the controller's behavior and system dynamics, providing sample, local, domain and universe level explanations via IF-THEN rules revealing the controller's decision logic and salience values quantifying the contribution of system states to control actions. Third, a large language model agent-supported user interface is developed to automatically analyze user requirements, select appropriate algorithms, interpret the generated explanations to a natural language report, and provide interactive consultation. Case studies on inverted pendulum system and Turtlebot obstacle avoidance demonstrate the effectiveness of the proposed method through simulated user experiments and quantitative comparisons with mainstream explainable control approaches.