Real-Time Episodic Memory Construction for Optimal Action Selection in Cognitive Robotics
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.
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: http://link.aps.org/doi/10.1103/PhysRev.106.620.
[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.
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: http://link.aps.org/doi/10.1103/PhysRev.106.620.
[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.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:79421", author = "Deon de Jager and Yahya Zweiri and Dimitrios Makris", title = "Real-Time Episodic Memory Construction for Optimal Action Selection in Cognitive Robotics", abstract = "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. ", volume = "14", number = "1", pages = "22-9", }