






Vol.4 , No. 5, Publication Date: Oct. 13, 2017, Page: 62-73
[1] | Nicoladie Tam, Department of Biological Sciences, University of North Texas, Denton, Texas, USA. |
A theoretical framework for autonomous self-detection and self-correction of unexpected error conditions is derived by incorporating the principles of operation in autonomous control in biological evolution. Using the biologically inspired principles, the time-dependent multi-dimensional disparity vector is used as a quantitative metric for detecting unexpected and unforeseeable error conditions without any external assistance. The disparity vector is a measure of the discrepancy between the expected outcome predicted by the autonomous system and the actual outcome in the real world. It is used as a measure to detect any unexpected or unforeseeable errors. The process for autonomous self-correction of the self-discovered errors is an optimization process to minimize the errors represented by the disparity vectors. The strategies for prioritizing the urgency of corrective actions are also provided in the theoretical derivations. The criteria for any change in direction of the corrective actions are also provided quantitatively. The criteria for the detection of the minimization and maximization of errors are also provided in the autonomous optimization process. The biological correspondences of the emotional responses in relation to the autonomic self-corrective feedback systems are also provided.
Keywords
Autonomous Systems, Emotional Processing, Error Detection, Error Correction, Emotional Feedback, Emotional Cognition, Emotional Intelligence
Reference
[01] | R. A. Chadwick, "Operating multiple semi-autonomous robots: Monitoring, responding, detecting," in Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2006, pp. 329-333. |
[02] | W. E. Haisler, J. A. Stricklin, and J. E. Key, "Displacement incrementation in non‐linear structural analysis by the self‐correcting method," International Journal for Numerical Methods in Engineering, vol. 11, pp. 3-10, 1977. |
[03] | S. B. Wicker, Error control systems for digital communication and storage vol. 1: Prentice hall Englewood Cliffs, 1995. |
[04] | R. Shadmehr, M. A. Smith, and J. W. Krakauer, "Error correction, sensory prediction, and adaptation in motor control," Annual review of neuroscience, vol. 33, pp. 89-108, 2010. |
[05] | C. E. Garcia and M. Morari, "Internal model control. A unifying review and some new results," Industrial & Engineering Chemistry Process Design and Development, vol. 21, pp. 308-323, 1982. |
[06] | M. Desmurget and S. Grafton, "Forward modeling allows feedback control for fast reaching movements," Trends in cognitive sciences, vol. 4, pp. 423-431, 2000. |
[07] | Y. Baryshnikov, E. Coffman, N. Seeman, and T. Yimwadsana, "Self-correcting self-assembly: growth models and the Hammersley process," in DNA Computing, ed: Springer, 2006, pp. 1-11. |
[08] | C. W. Tseng, W. Lu, and M. P. Baker, "Self-checking and self-correcting internal configuration port circuitry," ed: Google Patents, 2012. |
[09] | M. F. Angelo, G. D. Wisecup, and D. L. Collins, "System for validating a bios program and memory coupled therewith by using a boot block program having a validation routine," ed: Google Patents, 2003. |
[10] | H. Wasserman and M. Blum, "Software reliability via run-time result-checking," Journal of the ACM (JACM), vol. 44, pp. 826-849, 1997. |
[11] | P. Prata and J. G. Silva, "Algorithm based fault tolerance versus result-checking for matrix computations," in Fault-Tolerant Computing, 1999. Digest of Papers. Twenty-Ninth Annual International Symposium on, 1999, pp. 4-11. |
[12] | D. Tam, "EMOTION-I model: A biologically-based theoretical framework for deriving emotional context of sensation in autonomous control systems," Open Cybern Sys J, vol. 1, pp. 28-46, 2007. |
[13] | D. Tam, "EMOTION-II model: A theoretical framework for happy emotion as a self-assessment measure indicating the degree-of-fit (congruency) between the expectancy in subjective and objective realities in autonomous control systems," Open Cybern Sys J, vol. 1, pp. 47-60, 2007. |
[14] | D. N. Tam, "Computation in emotional processing: quantitative confirmation of proportionality hypothesis for angry unhappy emotional intensity to perceived loss," Cogn Comput, vol. 3, pp. 394-415, 2011/06/01 2011. |
[15] | N. D. Tam, "Quantification of happy emotion: Proportionality relationship to gain/loss," Psychol Behav Sci, vol. 3, pp. 60-67, April 6, 2014 2014. |
[16] | J. A. Stricklin and W. E. Haisler, "Formulations and solution procedures for nonlinear structural analysis," Computers & Structures, vol. 7, pp. 125-136, 1977. |
[17] | H. Mühlenbein and D. Schlierkamp-Voosen, "Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization," Evolutionary Computation, vol. 1, pp. 25-49, 1993/03/01 1993. |
[18] | M. Kumar, M. Husian, N. Upreti, and D. Gupta, "Genetic algorithm: Review and application," International Journal of Information Technology and Knowledge Management, vol. 2, pp. 451-454, 2010. |
[19] | R. Sivaraj and T. Ravichandran, "A review of selection methods in genetic algorithm," International journal of engineering science and technology, vol. 3, 2011. |
[20] | C. M. Fonseca and P. J. Fleming, "An overview of evolutionary algorithms in multiobjective optimization," Evolutionary computation, vol. 3, pp. 1-16, 1995. |
[21] | M. Tomassini, "Parallel and distributed evolutionary algorithms: A review," 1999. |
[22] | E. Zitzler, L. Thiele, E. Zitzler, E. Zitzler, L. Thiele, and L. Thiele, An evolutionary algorithm for multiobjective optimization: The strength pareto approach vol. 43: Citeseer, 1998. |
[23] | J. Davis, "An Introduction to Neural Networks," Journal of Cognitive Neuroscience, vol. 8, pp. 383-383, 1996/10/01 1996. |
[24] | S. Haykin, Neural Networks: A Comprehensive Foundation: Prentice Hall PTR, 1994. |
[25] | R. Hecht-Nielsen, "Theory of the backpropagation neural network," in Neural Networks, 1989. IJCNN., International Joint Conference on, 1989, pp. 593-605 vol. 1. |
[26] | J. J. Hopfield and D. W. Tank, ""Neural" computation of decisions in optimization problems," Biol Cybern, vol. 52, pp. 141-52, 1985. |
[27] | D. Psaltis, A. Sideris, and A. A. Yamamura, "A multilayered neural network controller," Control Systems Magazine, IEEE, vol. 8, pp. 17-21, 1988. |
[28] | D. Tam, "Theoretical Analysis of Cross-Correlation of Time-Series Signals Computed by a Time-Delayed Hebbian Associative Learning Neural Network," The Open Cybernetics & Systemics Journal, vol. 1, pp. 1-4, 2007. |
[29] | A. Cochocki and R. Unbehauen, Neural networks for optimization and signal processing: John Wiley & Sons, Inc., 1993. |
[30] | S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, "Optimization by simulated annealing," science, vol. 220, pp. 671-680, 1983. |
[31] | P. J. Van Laarhoven and E. H. Aarts, Simulated annealing: theory and applications vol. 37: Springer Science & Business Media, 1987. |
[32] | S. Kirkpatrick, "Optimization by simulated annealing: Quantitative studies," Journal of statistical physics, vol. 34, pp. 975-986, 1984. |
[33] | B. Kosko, "Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence/book and disk," Vol. 1 Prentice hall, 1992. |
[34] | R. Tong, "A control engineering review of fuzzy systems," Automatica, vol. 13, pp. 559-569, 1977. |
[35] | C. C. Lee, "Fuzzy logic in control systems: fuzzy logic controller. II," Systems, Man and Cybernetics, IEEE Transactions on, vol. 20, pp. 419-435, 1990. |
[36] | T. J. Ross, Fuzzy logic with engineering applications: John Wiley & Sons, 2009. |
[37] | N. D. Tam, "Quantitative assessment of sad emotion," Psychol Behav Sci, vol. 4, pp. 36-43, February 12, 2015 2015. |
[38] | N. D. Tam, "Quantification of happy emotion: Dependence on decisions," Psychol Behav Sci, vol. 3, pp. 68-74, April 6, 2014 2014. |
[39] | N. D. Tam, "Quantification of fairness perception by including other-regarding concerns using a relativistic fairness-equity model," Adv in Soc Sci Research J, vol. 1, pp. 159-169, 2014. |
[40] | N. D. Tam, "Rational decision-making process choosing fairness over monetary gain as decision criteria," Psychol Behav Sci, vol. 3, pp. 16-23, 2014. |
[41] | N. D. Tam, "Quantification of fairness bias in relation to decisions using a relativistic fairness-equity model," Adv in Soc Sci Research J, vol. 1, pp. 169-178, 2014. |
[42] | N. D. Tam and K. M. Smith, "Cognitive computation of jealous emotion," Psychology and Behavioral Sciences, vol. 3, pp. 1-7, Dec. 31, 2014 2014. |
[43] | N. D. Tam, "A decision-making phase-space model for fairness assessment," Psychol Behav Sci, vol. 3, pp. 8-15, 2014. |
[44] | T. Balch and M. Hybinette, "Social potentials for scalable multi-robot formations," in Robotics and Automation, 2000. Proceedings. ICRA'00. IEEE International Conference on, 2000, pp. 73-80. |
[45] | C. Breazeal and B. Scassellati, "A context-dependent attention system for a social robot," rn, vol. 255, p. 3, 1999. |
[46] | M. P. Michalowski, S. Sabanovic, and R. Simmons, "A spatial model of engagement for a social robot," in Advanced Motion Control, 2006. 9th IEEE International Workshop on, 2006, pp. 762-767. |
[47] | C. Bartneck and J. Forlizzi, "A design-centred framework for social human-robot interaction," in Proceedings of the 13th IEEE International Workshop on Robot and Human Interactive Communication, 2004, pp. 31-33. |
[48] | C.-Y. Chen and P.-H. Huang, "RETRACTED: Review of an autonomous humanoid robot and its mechanical control," Journal of Vibration and Control, vol. 18, pp. 973-982, 2012. |
[49] | G. Bilbeisi, N. Al-Madi, and F. Awad, "PSO-AG: A Multi-Robot Path Planning and obstacle avoidance algorithm," in Applied Electrical Engineering and Computing Technologies (AEECT), 2015 IEEE Jordan Conference on, 2015, pp. 1-6. |
[50] | D. Fox, W. Burgard, H. Kruppa, and S. Thrun, "A probabilistic approach to collaborative multi-robot localization," Autonomous robots, vol. 8, pp. 325-344, 2000. |
[51] | M. Brambilla, E. Ferrante, M. Birattari, and M. Dorigo, "Swarm robotics: a review from the swarm engineering perspective," Swarm Intelligence, vol. 7, pp. 1-41, 2013. |
[52] | K. Lerman, A. Martinoli, and A. Galstyan, "A review of probabilistic macroscopic models for swarm robotic systems," in Swarm robotics, ed: Springer, 2005, pp. 143-152. |
[53] | L. Bayindir and E. Sahin, "A review of studies in swarm robotics," Turkish Journal of Electrical Engineering, vol. 15, pp. 115-147, 2007. |
[54] | Y. Mohan and S. Ponnambalam, "An extensive review of research in swarm robotics," in Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on, 2009, pp. 140-145. |
[55] | N. D. Tam, "EMOTION-III model: A theoretical framework for social empathic emotions in autonomous control systems," Open Cybern Sys J, vol. in press, 2016. |
[56] | K. M. Lee, W. Peng, S. A. Jin, and C. Yan, "Can robots manifest personality?: An empirical test of personality recognition, social responses, and social presence in human–robot interaction," Journal of communication, vol. 56, pp. 754-772, 2006. |
[57] | K. M. Lee, Y. Jung, J. Kim, and S. R. Kim, "Are physically embodied social agents better than disembodied social agents?: The effects of physical embodiment, tactile interaction, and people's loneliness in human–robot interaction," International Journal of Human-Computer Studies, vol. 64, pp. 962-973, 2006. |