By Aaron Sloman (auth.), Michael Anderson MSc, PhD, Bernd Meyer Dr rer nat, Patrick Olivier MA, MSc, PhD (eds.)
Diagrams are crucial in so much fields of human job. there's substan tial curiosity in diagrams and their use in lots of educational disciplines for the aptitude merits they could confer on a variety of initiatives. Are we now capable of declare that we've got a technological know-how of diagrams-that is, a technology which takes the character of diagrams and their use because the significant phenom ena of curiosity? If we've got a technological know-how of diagrams it truly is definitely constituted from a number of disciplines, together with cognitive technology, psychology, man made intelligence, good judgment, arithmetic, and others. If there's a technological know-how of diagrams, then like different sciences there's an appli cations, or engineering, self-discipline that exists along the technology. Applica tions and engineering supply assessments of the theories and ideas chanced on by means of the technology and expand the scope of the phenomena to be studied by means of gen erating new makes use of of diagrams, new media for featuring diagrams, or novel periods of diagram. This functions and engineering aspect of the technological know-how of di agrams additionally contains a number of disciplines, together with schooling, structure, computing device technology, arithmetic, human-computer interplay, wisdom ac quisition, image layout, engineering, background of technology, facts, drugs, biology, and others.
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Additional info for Diagrammatic Representation and Reasoning
This essential knowledge includes generalisations about invariants, inventories of special cases, definitions of symmetry, knowledge of algebraic relations, generalisations of prior conclusions, and knowledge of problem-solving strategies and heuristics. Purely diagrammatic mathematical reasoning is an oxymoron, since mathematical reasoning always entails drawing explicit conclusions. It is desirable to establish an inventory of the knowledge that, in addition to the knowledge of space built into the programming language of my system, is needed to devise methods that can achieve human-like reasoning about geometry.
Nash-Webber (Eds), Theoretical issues in natural language processing (TINLAP). Cambridge, MA: MIT Press, pp. 431-439. Reprinted in . 12. Sloman, A. (1978). The computer revolution in philosophy: Philosophy, science and models of mind. Hassocks, UK: Harvester Press. 13. Sloman, A. (1985). Why we need many knowledge representation formalisms. In M. ), Research and development in expert systems. Cambridge, UK: Cambridge University Press, pp. 163-183. 14. Sloman, A. (1989). On designing a visual system (towards a Gibsonian computational model of vision).
What if we force it to hold by choosing B and D accordingly, as in Fig. 3? Then it will be observed that the quadrilateral is uniquely determined, and it satisfies the midpoint property. It is thus possible to construct a circumscribing quadrilateral obeying the parallelogram property around an arbitrary parallelogram. We have also learned that the situation has only a few degrees of freedom: selecting one quadrilateral vertex fixes the midpoint quadrilateral for a given parallelogram. The foregoing experiments have not led to a complete understanding of why the QT should hold, but have provided information about the quadrilateral-parallelogram relationship.
Diagrammatic Representation and Reasoning by Aaron Sloman (auth.), Michael Anderson MSc, PhD, Bernd Meyer Dr rer nat, Patrick Olivier MA, MSc, PhD (eds.)