Actively Obtaining Environmental Feedback for Autonomous Action Evaluation Without Predefined Measurements

2026-01-04

arXiv.org

AI Summary

In open and dynamic environments, agents often struggle to evaluate their actions due to the absence of predefined feedback. This work proposes an active feedback acquisition model that autonomously discovers, filters, and validates effective feedback signals by analyzing the environmental changes induced by its actions, without relying on external rewards or pre-specified metrics. The approach incorporates an intrinsic-goal-driven self-triggering mechanism—guided by objectives such as accuracy and efficiency—to enable autonomous action planning. Experimental results demonstrate that the model substantially enhances the efficiency and robustness of feedback identification, allowing agents to rapidly focus on and acquire high-quality feedback without external supervision.

Technology Category

• Machine Learning: Active Learning
• Multiagent Systems: Modeling other Agents
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• environmental feedback
• autonomous action evaluation
• predefined measurements
• open-ended environments
• intelligent agents

Innovation

• active feedback acquisition
• environmental interaction
• self-triggering mechanism
• autonomous action evaluation
• unsupervised feedback discovery