JACIII Vol.16 No.2 pp. 358-367
doi: 10.20965/jaciii.2012.p0358


The Spiral Discovery Method: An Interpretable Tuning Model for CogInfoCom Channels

Adam Csapo*,** and Péter Baranyi*

*Computer and Automation Research Institute, Hungarian Academy of Sciences, Kende u. 13-17, Budapest 1111, Hungary

**Department of Telecommunications and Media Informatics, Budapest University of Technology and Economic, Magyar Tudosok Krt. 2, Budapest 1117, Hungary

September 15, 2011
November 15, 2011
March 20, 2012
cognitive infocommunications, CogInfoCom channels, CogInfoCom messages, cognitive artifacts
Cognitive Infocommunications (CogInfoCom) messages that are used to carry information on the state of the same high-level concept can be regarded as belonging to a CogInfoCom channel. Such channels can be generated using any kind of parametric model. By changing the values of the parameters, it is possible to arrive at a large variety of CogInfoCom messages, a subset of which can belong to a CogInfoCom channel – provided they are perceptually well-suited to the purpose of conveying information on the same highlevel concept. Thus, for any CogInfoCom channel, we may speak of a parameter space and a perceptual space that is created by the totality of messages in the CogInfoCom channel. In this paper, we argue that in general, the relationship between the parameter space and the perceptual space is highly non-linear. For this reason, it is extremely difficult for the designer of a CogInfoCom channel to tune the parameters in such a way that the resulting CogInfoCom messages are perceptually continuous, and suitable to carry information on a single high-level concept. To address this problem, we propose a cognitive artifact that uses a rank concept available in tensor algebra to provide the designer of CogInfoCom channels with practical tradeoffs between complexity and interpretability. We refer to the artifact as the Spiral Discovery Method (SDM).
Cite this article as:
A. Csapo and P. Baranyi, “The Spiral Discovery Method: An Interpretable Tuning Model for CogInfoCom Channels,” J. Adv. Comput. Intell. Intell. Inform., Vol.16 No.2, pp. 358-367, 2012.
Data files:
  1. [1] M. Blattner, D. Sumikawa, and R. Greenberg, “Earcons and icons: Their structure and common design principles,” Human Computer Interaction, Vol.4, No.1, pp. 11-44, 1989.
  2. [2] A. Csapo and P. Baranyi, “A Unified Terminology for CogInfoCom Applications,” In 2nd IEEE Int. Conf. on Cognitive Infocommunications, pp. 1-6, 2011.
  3. [3] D. Norman, “Cognitive Artifacts,” pp. 17-38, Cambridge University Press, New York, NY, USA, 1991.
  4. [4] P. Barnard, “Bridging between basic theories and the artifacts of human-computer interaction,” pp. 103-127, Cambridge University Press, 1991.
  5. [5] H. Takagi, “Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation,” Proc. of the IEEE, Vol.89, No.9, pp. 1275-1296, 2001.
  6. [6] S. Barrass, “Auditory Information Design,” Ph.D. thesis, Australian National University, 1998.
  7. [7] L. D. Lathauwer, B. D. Moor, and J. Vandewalle, “A multi linear singular value decomposition,” SIAM J. on Matrix Analysis and Applications, Vol.21, No.4, pp. 1253-1278, 2000.
  8. [8] M. Ishteva, L. Lathauwer, P. Absil, and S. v. Huffel, “Dimensionality Reduction for Higher-Order Tensors: Algorithms and Applications,” Int. J. of Pure and Applied Mathematics, Vol.42, No.3, pp. 337-343, 2008.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 12, 2024