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An Episode Tracker for Cognitive Architectures

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Biologically Inspired Cognitive Architectures 2023 (BICA 2023)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1130))

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Abstract

This paper introduces the Episode Tracker Module, an encoding mechanism that tracks sensory information through space and time, building up high-level semantic representations called episodes. This module is aimed to extend the Cognitive Systems Toolkit (CST) as a reusable framework for building different cognitive models for episode detection. We created two instances of the episode tracker with two different mechanisms for identifying property categories (geographical regions). Each mechanism correctly induced a different episode detection dynamic. Overall, the Episode Tracker architecture provides a robust and flexible framework for episode detection.

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Notes

  1. 1.

    Martin et al. [12] originally used the term scene to what we are calling here a frame. We are using frame here to avoid ambiguity with using the term scene in scene-based episodes.

  2. 2.

    CST’s source is available in https://github.com/CST-Group/cst.

  3. 3.

    Codelets, first introduced in [10] and later enhanced in [7], are small segments of non-blocking code executed in a loop. Codelets run in parallel and are responsible for all data processing within the architecture.

  4. 4.

    Memory objects in CST hold any type of data structure to store information. Memory Objects are the canonical storage for data in the cognitive architecture. Different knowledge representation schemes might be used in each Memory Object.

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Acknowledgements

This project was supported by the Ministry of Science, Technology, and Innovation of Brazil, PPI-Softex grant # [01245.013778/2020-21]. The authors also thank CEPID/BRAINN (Proc. FAPESP 2013/07559-3).

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Authors and Affiliations

  1. University of Campinas (UNICAMP), Campinas, SP, Brazil

    Eduardo Yuji Sakabe, Anderson Anjos da Silva, Luiz Fernando Coletta, Esther Luna Colombini, Paula Dornhofer Paro Costa & Ricardo Ribeiro Gudwin

  2. University of the State of São Paulo (UNESP), Sorocaba, SP, Brazil

    Alexandre da Silva Simões

Authors
  1. Eduardo Yuji Sakabe
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  2. Anderson Anjos da Silva
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  3. Luiz Fernando Coletta
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  4. Alexandre da Silva Simões
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  5. Esther Luna Colombini
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  6. Paula Dornhofer Paro Costa
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  7. Ricardo Ribeiro Gudwin
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Corresponding author

Correspondence to Ricardo Ribeiro Gudwin .

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Editors and Affiliations

  1. Cybernetics Department, Moscow Engineering Physics Institute, Moscow, Russia

    Alexei V. Samsonovich

  2. College of Science and Technology, Ningbo University, Ningbo, China

    Tingting Liu

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Sakabe, E.Y. et al. (2024). An Episode Tracker for Cognitive Architectures. In: Samsonovich, A.V., Liu, T. (eds) Biologically Inspired Cognitive Architectures 2023. BICA 2023. Studies in Computational Intelligence, vol 1130. Springer, Cham. https://doi.org/10.1007/978-3-031-50381-8_81

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