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Principles of NoologyCausal Rules, Problem Solving, and Operational Representation

Principles of Noology: Causal Rules, Problem Solving, and Operational Representation [Building on the conceptual grounding framework set up by the previous chapter, this chapter illustrates the application of grounded operational representations for the formulation of causal rules. Causal rules are noologically efficacious entities that enable effective problem solving. Causal rules for effecting movement, propulsion, reflection, obstruction, penetration, attachment, etc. are described. The mechanism of incremental chunking for problem solving is also described using these causal rules. It is shown how a complex problem solving process can employ these rules built on grounded representations and how, as a result, a noological system can learn rapidly through language instructions because concepts are properly represented and understood at the ground level.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Principles of NoologyCausal Rules, Problem Solving, and Operational Representation

Part of the Socio-Affective Computing Book Series (volume 3)
Ho, Seng-Beng
Principles of Noology — Jun 29, 2016
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28 pages

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Publisher
Springer International Publishing
Copyright
© Springer International Publishing Switzerland 2016
ISBN
978-3-319-32111-0
Pages
191 –219
DOI
10.1007/978-3-319-32113-4_5
Publisher site
See Chapter on Publisher Site

Abstract

[Building on the conceptual grounding framework set up by the previous chapter, this chapter illustrates the application of grounded operational representations for the formulation of causal rules. Causal rules are noologically efficacious entities that enable effective problem solving. Causal rules for effecting movement, propulsion, reflection, obstruction, penetration, attachment, etc. are described. The mechanism of incremental chunking for problem solving is also described using these causal rules. It is shown how a complex problem solving process can employ these rules built on grounded representations and how, as a result, a noological system can learn rapidly through language instructions because concepts are properly represented and understood at the ground level.]

Published: Jun 29, 2016

Keywords: Causal rule; Conceptual grounding; Semantic grounding; Meaning; Problem solving; Incremental chunking; Monkey-and-bananas problem; Learning through language

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