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Real-Time Episodic Memory Construction for Optimal Action Selection in Cognitive Robotics

Authors: Deon de Jager, Yahya Zweiri, Dimitrios Makris


The three most important components in the cognitive architecture for cognitive robotics is memory representation, memory recall, and action-selection performed by the executive. In this paper, action selection, performed by the executive, is defined as a memory quantification and optimization process. The methodology describes the real-time construction of episodic memory through semantic memory optimization. The optimization is performed by set-based particle swarm optimization, using an adaptive entropy memory quantification approach for fitness evaluation. The performance of the approach is experimentally evaluated by simulation, where a UAV is tasked with the collection and delivery of a medical package. The experiments show that the UAV dynamically uses the episodic memory to autonomously control its velocity, while successfully completing its mission.

Keywords: Cognitive robotics, semantic memory, episodic memory, maximum entropy principle, particle swarm optimization.

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S. Lewandowsky and S. Farrell, Computational Modeling in Cognition: Principles and Practice. Sage Publications Inc., 2011, p. 357.
[2] J. R. Anderson, The Architecture of Cognition. Harvard University Press, 1983, p. 345.
[3] J. E. Laird, The SOAR Cognitive Architecture. The MIT Press, 2012.
[4] C. Eliasmith, How to Build a Brain (Oxford Series on Cognitive Models and Architecture). United States: Oxford University Press, 2013, p. 456.
[5] D. J. Blower, Information Processing - The Maximum Entropy Principle. CreateSpace Independent Publishing Platform, 2013.
[6] B. J. G. Baars, Nicole M., Fundamentals of Cognitive Neuroscience - A Beginner's Guide, Second ed. Academic Press - Elsevier, 2018.
[7] J. R. Anderson, D. Bothell, M. D. Byrne, S. Douglass, C. Lebiere, and Y. Qin, "An integrated theory of the mind," Psychological Review, vol. 111, 4, pp. 1036-1060, 2004.
[8] E. Tulving, "How many memory systems are there," American Psychologist, vol. 40, pp. 385-398, 1985.
[9] G. A. Radvansky, Human Memory, Third ed. Routledge, 2017.
[10] R. Eberhart and J. Kennedy, "A new optimizer using particle swarm theory," in Micro Machine and Human Science, 1995. MHS '95., Proceedings of the Sixth International Symposium on, 4-6 Oct 1995 1995, pp. 39-43, doi: 10.1109/mhs.1995.494215.
[11] C. Wei-Neng, Z. Jun, H. S. H. Chung, Z. Wen-Liang, W. Wei-gang, and S. Yu-Hui, "A Novel Set-Based Particle Swarm Optimization Method for Discrete Optimization Problems," Evolutionary Computation, IEEE Transactions on, vol. 14, no. 2, pp. 278-300, 2010, doi: 10.1109/tevc.2009.2030331.
[12] J. Langeveld and A. Engelbrecht, "Set-based particle swarm optimization applied to the multidimensional knapsack problem," (in English), Swarm Intelligence, vol. 6, no. 4, pp. 297-342, 2012/12/01 2012, doi: 10.1007/s11721-012-0073-4.
[13] E. T. Jaynes, "Information Theory and Statistical Mechanics," Physical Review, vol. 106, no. 4, pp. 620-630, 1957. (Online). Available:
[14] C. E. Shannon, "A mathematical theory of communication," Bell System Technical Journal, The, vol. 27, no. 4, pp. 623-656, 1948, doi: 10.1002/j.1538-7305.1948.tb00917.x.
[15] D. De Jager, "UAV Benchmark mission 2," ed, 2019, p. Video of UAV Benchmark mission 2.