Published February 28, 2024 | Version CC-BY-NC-ND 4.0
Journal article Open

Long Horizon Episodic Decision Making for Cognitively Inspired Robots

Creators

  • 1. Department of Mechanical Engineering, MIT WPU, Pune (Maharashtra), India.

Contributors

Contact person:

  • 1. Department of Mechanical Engineering, MIT WPU, Pune (Maharashtra), India
  • 2. Department of Brain, Cognition and Computation Lab, IIIT, Hyderabad (Telangana), India.
  • 3. Department of Mechanical Engineering, MIT WPU, Pune (Maharashtra), India.

Description

Abstract: The Human decision-making process works by recollecting past sequences of observations and using them to decide the best possible action in the present. These past sequences of observations are stored in a derived form which only includes important information the brain thinks might be useful in the future, while forgetting the rest. we propose an architecture that tries to mimic the human brain and improve the memory efficiency of transformers by using a modified Transformer XL architecture which uses Automatic Chunking which only attendsto the relevant chunksin the transformer block. On top ofthis,we useForget Span which is technique to remove memories that do not contribute to learning. We also theorize the technique of Similarity based forgetting to remove repetitive memories. We test our model in various tasks that test the abilities required to perform well in a human-robot collaboration scenario.

Files

A108204011223.pdf

Files (749.4 kB)

Name Size Download all
md5:c567002015f2b337d0170c2f8771ae41
749.4 kB Preview Download

Additional details

Identifiers

DOI
10.54105/ijainn.B1082.04020224
EISSN
2582-7626

Dates

Accepted
2024-02-15
Manuscript received on 01 December 2023 | Revised Manuscript received on 10 December 2024 | Manuscript Accepted on 15 February 2024 | Manuscript published on 28 February 2024.

References

  • Stephanie CY Chan, Marissa C Applegate, Neal W Morton, Sean M Polyn, and Kenneth A Norman (2017) 'Lingering representations of stimuli influence recall organization', Neuropsychologia, vol. 97, pp. 72–82, DOI: 10.1016/j.neuropsychologia.2017.01.029
  • Sols, I. et al. (2017) 'Event Boundaries Trigger Rapid Memory Reinstatement of the Prior Events to Promote Their Representation in Long-Term Memory', Current Biology, 27(22), pp. 3499-3504.e4. doi: 10.1016/j.cub.2017.09.057.
  • Aida Nematzadeh, Sebastian Ruder, and Dani Yogatama (2020) 'On memory in human and artificial language processing systems', ICLR 2020: In Bridging AI and Cognitive Science Workshop, 26 April1 May. Available at: https://api.semanticscholar.org/CorpusID:221088218.
  • Pleines, M. et al. (2023) 'TransformerXL as Episodic Memory in Proximal Policy Optimization', GitHub Repository. Available at: https://github.com/MarcoMeter/episodic-transformer-memory-ppo.
  • Chevalier-Boisvert et al. (2023) 'Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for GoalOriented Tasks', CoRR, abs/2306.13831.
  • Juliani, A. et al. (2020) 'Unity: A general platform for intelligent agents', arXiv preprint arXiv:1809.02627. Available at: https://arxiv.org/pdf/1809.02627.pdf.
  • Brockman, G. et al. (2016) 'OpenAI Gym', arXiv Eprint arXiv:1606.01540. Available at: http://arxiv.org/abs/1606.01540.
  • Patil, Dr. K., & Kulkarni, Dr. M. S. (2019). Artificial Intelligence in Financial Services: Customer Chatbot Advisor Adoption. In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 1, pp. 4296–4303). https://doi.org/10.35940/ijitee.a4928.119119
  • Hudaa, S., Setiyadi, D. B. P., Lydia, E. L., Shankar, K., Nguyen, P. T., Hashim, W., & Maseleno, A. (2019). Natural Language Processing utilization in Health care. In International Journal of Engineering and Advanced Technology (Vol. 8, Issue 6s2, pp. 1117–1120). https://doi.org/10.35940/ijeat.f1305.0886s219
  • Vatan, Sharma, A., & Goyal, S. (2019). Artificial Intelligence on the Move: A Revolutionary Technology. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 4, pp. 12112– 12120). https://doi.org/10.35940/ijrte.d7293.118419
  • Mishra, S. (2022). A Comparative Analysis of Diabetes Prediction using Different Machine Learning Algorithms. In Indian Journal of Artificial Intelligence and Neural Networking (Vol. 2, Issue 5, pp. 1– 7). https://doi.org/10.54105/ijainn.e1057.082522
  • P A, J., & N, A. (2022). Faceium–Face Tracking. In Indian Journal of Data Communication and Networking (Vol. 2, Issue 5, pp. 1–4 https://doi.org/10.54105/ijdcn.b3923.082522