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SF-EM

The proposed SF-EM stably performs over a long period of time and accepts user feedback. The following are the key features of SF-EM:

  • An adaptive decay factor to enhance the stability of the learning process of the memory architecture.
  • A feedback mechanism to reflect user feedback.
  • A home service provision framework for robot and IoT collaboration.

Requirements

  • Python >= 3.6.5

Experiment

For the performance of feedback, run the following:

python3 SFEM.py

Then, you will get the following memory modulation result. Memory

For the stabilized memory strength with the proposed adaptive decay factor, you might need to modify a part of codes in SFEM.py. Then, you will get the following result: Strength

You can also run the followings to check the performance of previous memory models.

python3 EMART.py
python3 FeedbackART.py
python3 FusionART.py

Citation

Please consider citing this project in your publications if you find this helpful. The following is the BibTeX.

@article{kim2018a,
  title={A Stabilized Feedback Episodic Memory (SF-EM) and Home Service Provision Framework for Robot and IoT Collaboration},
  author={Uehwan Kim and Jong-Hwan Kim},
  journal={IEEE Transactions on Cybernetics, Early Access},
  year={2018}
}

Acknowledgments

This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No.2016-0-00563, Research on Adaptive Machine Learning Technology Development for Intelligent Autonomous Digital Companion)