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Actively Obtaining Environmental Feedback for Autonomous Action Evaluation Without Predefined Measurements

Hong Su

TL;DR

This work addresses the challenge of feedback in open-ended environments where predefined measurements are unavailable. It introduces the Actively Feedback Getting model, which treats action-induced environmental differences as the universal carrier of feedback, and builds a pipeline of difference-driven detection, active screening, and accumulated learning to identify, validate, and reuse feedback signals. A self-contained mechanism of internal action triggers drives proactive exploration, while active intervention accelerates causal factor discovery and reduces reliance on passive observation. Experimental results demonstrate improved efficiency in factor identification and statistically significant reductions in the number of required queries when using active feedback acquisition, highlighting the framework's potential for more autonomous and robust intelligent agents in dynamic settings.

Abstract

Obtaining reliable feedback from the environment is a fundamental capability for intelligent agents to evaluate the correctness of their actions and to accumulate reusable knowledge. However, most existing approaches rely on predefined measurements or fixed reward signals, which limits their applicability in open-ended and dynamic environments where new actions may require previously unknown forms of feedback. To address these limitations, this paper proposes an Actively Feedback Getting model, in which an AI agent proactively interacts with the environment to discover, screen, and verify feedback without relying on predefined measurements. Rather than assuming explicit feedback definitions, the proposed method exploits action-induced environmental differences to identify target feedback that is not specified in advance, based on the observation that actions inevitably produce measurable changes in the environment. In addition, a self-triggering mechanism, driven by internal objectives such as improved accuracy, precision, and efficiency, is introduced to autonomously plan and adjust actions, thereby enabling faster and more focused feedback acquisition without external commands. Experimental results demonstrate that the proposed active approach significantly improves the efficiency and robustness of factor identification.

Actively Obtaining Environmental Feedback for Autonomous Action Evaluation Without Predefined Measurements

TL;DR

This work addresses the challenge of feedback in open-ended environments where predefined measurements are unavailable. It introduces the Actively Feedback Getting model, which treats action-induced environmental differences as the universal carrier of feedback, and builds a pipeline of difference-driven detection, active screening, and accumulated learning to identify, validate, and reuse feedback signals. A self-contained mechanism of internal action triggers drives proactive exploration, while active intervention accelerates causal factor discovery and reduces reliance on passive observation. Experimental results demonstrate improved efficiency in factor identification and statistically significant reductions in the number of required queries when using active feedback acquisition, highlighting the framework's potential for more autonomous and robust intelligent agents in dynamic settings.

Abstract

Obtaining reliable feedback from the environment is a fundamental capability for intelligent agents to evaluate the correctness of their actions and to accumulate reusable knowledge. However, most existing approaches rely on predefined measurements or fixed reward signals, which limits their applicability in open-ended and dynamic environments where new actions may require previously unknown forms of feedback. To address these limitations, this paper proposes an Actively Feedback Getting model, in which an AI agent proactively interacts with the environment to discover, screen, and verify feedback without relying on predefined measurements. Rather than assuming explicit feedback definitions, the proposed method exploits action-induced environmental differences to identify target feedback that is not specified in advance, based on the observation that actions inevitably produce measurable changes in the environment. In addition, a self-triggering mechanism, driven by internal objectives such as improved accuracy, precision, and efficiency, is introduced to autonomously plan and adjust actions, thereby enabling faster and more focused feedback acquisition without external commands. Experimental results demonstrate that the proposed active approach significantly improves the efficiency and robustness of factor identification.
Paper Structure (45 sections, 10 equations, 3 figures)

This paper contains 45 sections, 10 equations, 3 figures.

Table of Contents

  1. Introduction
  2. Related Works
  3. Predefined Feedback and Reward Modeling
  4. Active Perception and Active Learning
  5. Causal Discovery and Difference-Based Reasoning
  6. Autonomous Agents and Intrinsic Motivation
  7. Summary
  8. Actively Feedback Getting Model
  9. Problem Definition and Assumptions
  10. Difference-Driven Feedback Detection
  11. Degree of Difference
  12. Initial Screening Mechanism
  13. Active Feedback Screening and Validation
  14. Active Feedback Screening
  15. Action--Result Screening
  16. ...and 30 more sections

Figures (3)

  • Figure 1: Overview of the proposed Actively Feedback Getting model
  • Figure 2: Comparison of semantic similarity to the reference causal explanation between the difference-oriented and direct reasoning strategies
  • Figure 3: Comparison of query efficiency between the active method and passive observation across trials