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Similarity based task timely termination method for image based intelligent agents.

Sheng Jie1, Xing Huang1, Chengxi Jing1

  • 1Zhejiang Gongshang University, SIEE, Hangzhou, 310018, China.

Scientific Reports
|December 31, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a similarity-based method to stop image-based intelligent agents from continuing tasks after completion, reducing resource waste. The approach uses structural similarity to determine task finalization, improving efficiency and preventing unnecessary actions.

Keywords:
ApplicationIntelligent agentLarge language modelTask terminationTimely

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Robotics

Background:

  • Large language models (LLMs) can hallucinate, causing intelligent agents to continue tasks unnecessarily, leading to resource wastage.
  • Current methods lack effective mechanisms for timely task termination in image-based intelligent agents.
  • Confusion in agents arises from LLM hallucinations or unclear task goals, resulting in inefficient operation.

Purpose of the Study:

  • To propose a similarity-based task timely termination method for image-based intelligent agents.
  • To reduce resource consumption (time, tokens, hardware) by preventing agents from overworking.
  • To enhance the reliability and efficiency of intelligent agents in completing image-based tasks.

Main Methods:

  • Recording scenario states after each sub-task completion.
  • Comparing current scenario states with the fully completed task state using a structural similarity method.
  • Quantifying and standardizing the comparison into a structural similarity index for termination judgment.
  • Categorizing agent types based on models and creating an image-based agent task dataset.

Main Results:

  • Image-based agents demonstrated an average reduction of 1.94 steps in task completion.
  • Significant reductions in time costs and token costs were observed.
  • The method effectively mitigated negative actions caused by agent hallucinations.

Conclusions:

  • The proposed similarity-based method effectively terminates tasks timely for image-based intelligent agents.
  • This approach significantly reduces resource waste and improves task completion quality.
  • The method offers a practical solution for enhancing the performance and efficiency of intelligent agents.