Interactive evolutionary computation for analyzing human characteristics
Hideyuki Takagi: Interactive evolutionary computation for analyzing human characteristics. Symposium on Emergent Trends in Artificial Intelligence & Robotics, (SETINAIR2013), Kosice, Slovakia, (Sept. 15-17, 2013).
We discuss the importance of establishing awareness science and show the idea of using interactive evolutionary computation (IEC) as a tool for analyzing awareness mechanism and making awareness models. First, we describe the importance of human factors in computational intelligence and that IEC is one of approaches for the so-called humanized computational intelligence. Second, we show examples that IEC is used as an analysis tool for human science. As analyzing human awareness mechanism is in this kind of analyzing human characteristics and capabilities, IEC may be able to be used for this purpose. Based on this expectation, we express one idea for analyzing the awareness mechanism. This idea is to make an equivalent model of an IEC user using a learning model and find latent variables that connect inputs and outputs of the user model and that help to understand or explain the inputs-outputs relationship. Although there must be several definitions of awareness, this idea is based on one definition that awareness is to find out unknown variables that helps our understanding. If we establish a method for finding the latent variables automatically, we can realize an awareness model in computer.
R. Dawkins, The Blind Watchmaker, Longman, Essex, UK, 1986.
H. Takagi, “Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation,” Proceedings of the IEEE, vol. 89, no. 9, pp. 1275–1296, 2001. View at Scopus http://www.scopus.com/scopus/inward/record.url?eid=2-s2.0-0000530063&partnerID=K84CvKBR&rel=3.0.0&md5=93e8bb19336ec5e664b9b1dfc8fb352c
K. Aoki and H. Takagi, “3-D CG lighting with an interactive GA,” in Proceedings of the 1st International Conference on Conventional and Knowledge-based Intelligent Electronic Systems (KES ’97), pp. 296–301, Adelaide, Australia, 197.
K. Aoki and H. Takagi, “Interactive GA-based design support system for lighting design in 3D computer graphics,” Transactions of IEICE, vol. 81, no. 7, pp. 1601–1608, 1.
S. R. Kay, A. Fiszbein, and L. A. Opler, “The positive and negative syndrome scale (PANSS) for schizophrenia,” Schizophrenia Bulletin, vol. 13, no. 2, pp. 261–276, 1987. View at Scopus http://www.scopus.com/scopus/inward/record.url?eid=2-s2.0-0023606101&partnerID=K84CvKBR&rel=3.0.0&md5=637b5fd8cd03e679f11e9640e45afeb3
H. Takagi and M. Ohsaki, “Interactive evolutionary computation-based hearing aid fitting,” IEEE Transactions on Evolutionary Computation, vol. 11, no. 3, pp. 414–427, 2007. View at Publisher · View at Google Scholar · View at Scopus http://www.scopus.com/scopus/inward/record.url?eid=2-s2.0-34250219592&partnerID=K84CvKBR&rel=3.0.0&md5=0a1112100f485499c709b077bc27c0b6
P. Legrand, C. Bourgeois-Republique, V. Péan et al., “Interactive evolution for cochlear implants fitting,” Genetic Programming and Evolvable Machines, vol. 8, no. 4, pp. 319–354, 2007. View at Publisher · View at Google Scholar · View at Scopus http://www.scopus.com/scopus/inward/record.url?eid=2-s2.0-36148988338&partnerID=K84CvKBR&rel=3.0.0&md5=6e6d37830eac1fb9f67df6eb8fa24919
H. Takagi, S. Wang, and S. Nakano, “Proposal for a framework for optimizing artificial environments based on physiological feedback,” Journal of Physiological Anthropology and Applied Human Science, vol. 24, no. 1, pp. 77–80, 2005. View at Publisher · View at Google Scholar · View at Scopus http://www.scopus.com/scopus/inward/record.url?eid=2-s2.0-15244355126&partnerID=K84CvKBR&rel=3.0.0&md5=23171793d4e06ec34c5b87db02eddb18
http://dx.doi.org/10.1155/2012/694836 Volltext, open acess article