MARKET DEFINITION PARADIGM:
AN OVERVIEW
Knowledge is similar to acquired human vision that is devoid of any illusions about objects in space. Knowledge comprises the conduct of rule-based classification of resource(s), awareness creation, users’ needs assessment, and generation of knowledge transfer metrics. This means that the ability to see things in depth does not depend on familiarity with the objects but requires a market point solution that can extract distance information and address the well-known distance gradient phenomenon (Lindsay and Norman, 1977).
The perception of multi-dimensional space is usually through the use of two-dimensional hard and soft computing models of systems, which lack a definition (focus and convergence) interpretation like the human eye. This approach will not reveal that:
i) distant objects take up a smaller angle
than they do when they close up
ii) textures change with distance and viewing angle, and
iii) lines converge in the distance
There is an urgent need to develop a paradigm for interpreting space when its perceptions by different machines may create an apparent convergence out of a network of blind spots, which lack focus. This “convergence bug” is the uncertainty about distance information when a pattern with converging lines or edges is perceived to have been generated by contours of an unknown multi-dimensional object. According to Jin (2005):
To acquire, understand, and reuse knowledge is one of the most important features of intelligent systems. Unfortunately, knowledge representation in humans and different machine systems could be very different, which makes it difficult to transfer knowledge between humans and machines, as well as between different machine systems.
The market definition paradigm is subsequently conceived as capable of converting data into one out of six exhaustive market scenarios just like the retina-like sensitive interface meant to be introduced between the hard and soft computing components of an intelligent information system(s) (Yesufu and Yesufu, 2003). It obtains sensory information, which is devoid of the convergence bug, from a typical two-dimensional data for creating, recreating, and transforming the distance gradient phenomenon of space. It therefore gives a consistent basis for the universal and rational transfer of knowledge by interpreting divergence from right angles of intersecting lines formed by contours of objects as distance information. This paradigm is also capable of detecting impossible organizations and fictitious data, and it is used for carrying out features’ analysis, extraction, and correction, and solving other problems encountered in piecing together the sensory information for producing a consistent image of the world and the universe.
References
Jin, Y. (2005) Guest Editorial: Special Issue on Knowledge Extraction and Incorporation in Evolutionary Computation, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, p. 129.
Lindsay, P.H. and Norman, D.A. (1977): Human Information Processing: An Introduction to Psychology, second edition, Academic Press Inc., pp. 29 -32.
Yesufu, O.A. and Yesufu, T.K. (2003): Development of the Market Paradigm for analyzing Systems, Available Online www.ssrn.com/abstract=437181.