Agencies of Intelligence: From the Macro to the Nano
- 1.5k Downloads
‘Homo sapiens’ (Latin for ‘Wise Man’) is what we call our species. Wisdom and Intelligence stands at the center of how we define ourselves. But what is intelligence, and how does it influence an agent’s ability to face the world? In this chapter, we review an array of perspectives from the outer behavioral aspects of intelligence such as generalization, optimization and learning to its inner compositional that emphasizes intelligence in terms of networking and connectivity. Our journey will walk us through the concept of omnipotency where an entity has it all, knows it all, and does it all; to the nanopotency where the entity has little, knows little, and does little. From the original manifestation of the human dream to create the omnipotent being, we now come to its recent realization that perhaps less can be more. We will illustrate by sharing a few of our findings on traditionally hard problems such as robotics, urban traffic, fault detection and isolation, portfolio selection and judicial/medical decision making to the more evasive and humanly profound problems such as the atherosclerosis and cancer.
KeywordsOmnipotent Intelligence Learning Generalization Optimization Complex Systems Agent
Unable to display preview. Download preview PDF.
- 3.Akbarzadeh-T., M.-R., Jamshidi, M.: Evolutionary Fuzzy Control of a Flexible Link. Journal of Intelligent Automation and Soft Computing 9(3), 181–214 (1997)Google Scholar
- 5.Ghaemi., M., Akbarzadeh-T., M.-R.: Indirect Adaptive Interval Type-2 Fuzzy Sliding Mode Control for a Class of Uncertain Nonlinear Systems. Iranian Journal of Fuzzy Systems 11(5), 1–21 (2014)Google Scholar
- 6.Fartashtoloue, S.: Designing a Dynamic Growing General Type-2 Fuzzy Neural Controller for a Class of Nonlinear Systems with Experimental Implementation on a 3PSP Parallel Robot. M.S.Thesis, Ferdowsi University of Mashhad (2013)Google Scholar
- 8.Sabahi, F., Akbarzadeh-T., M.-R.: Comparative Evaluation of Risk Factors in Coronary Heart Disease Based on Fuzzy Probability-Validity Modeling. Journal of Zanjan University of Medical Sciences and Health Services 22(91), 73–83 (2014)Google Scholar
- 10.Tayarani, M.H., Akbarzadeh-T., M.-R.: Magnetic Inspired Optimization Algorithms: Operators and Structures. Swarm and Evolutionary Computation (accepted 2014)Google Scholar
- 16.Akramizadeh, A., Akbarzadeh-T., M.R., Khademi, M.: Fuzzy Discrete Event System Modeling and Temporal Fuzzy Reasoning in Urban Traffic Control. In: Proceedings of the 2004 World Automation Congress and Fifth International Symposium on Intelligent Automation and Control, Seville, Spain, June 28-July 1 (2004)Google Scholar
- 17.Sengstacken, A.J., DeLaurentis, D.A., Akbarzadeh-T., M.R.: Optimization of Shared Autonomy Vehicle Control Architectures for Swarm Operations. IEEE Transactions on Systems, Man, and Cybernetics 40(4), 145–1157 (2010)Google Scholar
- 18.Hartmann, A.K., Weigt, M.: Phase Transitions in Combinatorial Optimization Problems, Basis, Algorithms and Statistical Mechanics. Wiley-VCH Verlag Co. (2005)Google Scholar
- 19.Vafaei Jahan, M., Akbarzadeh-T., M.R.: From Local Search to Global Conclusions: Migrating Spin Glass-based Distributed Portfolio Selection. IEEE Transactions on Evolutionary Computations (2009)Google Scholar
- 20.Rowhanimanesh, A., Akbarzadeh-T., M.-R.: Control of Low-Density Lipoprotein Concentration in the Arterial Wall by Proportional Drug-Encapsulated Nanoparticles. IEEE Transactions on Nanobioscience (2012)Google Scholar
- 21.Radyraz, N.: Bio-inspired Nanonetworks with Local Fuzzy Vision for Targeted Cancer Drug Delivery. M.S. Thesis, Islamic Azad University, Mashhad Branch (2014)Google Scholar