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**10** 

John Ronczka

*Australia* 

**W semantic Temporal Entanglement** 

*Australian Maritime College, University of Tasmania* 

**Modelling for Human - Machine Interfaces** 

This Chapter focuses on 'Command—causalities—consequences Wisdom' (C3W) semantics temporal entanglement modeling using 'Wisdom open system semantic intelligence' (WOSSI) methodology (Ronczka, 2009). A feature might be a Rubik–Schlangen type three– dimensional system and wisdom–based delivery engine acting as a continuum with

WOSSI is a mapping system that allows identification of wisdom from lower order delivery engines and associated domains to acquire the information, knowledge, reasoning, and understanding whilst in an open–system context. WOSSI mapping has the outcome of minimising the influence of 'de Montaigne' paradoxes. That is, a possible negative outcome: '*nothing is so firmly believed as that which we least know'*, (Collins, 2002) that may drive conflicting tangibles and intangibles such as 'actual monetary benefits' and 'Willingness–to– pay' but still providing foresight based on evidence based 'knowledge—information—

A modified Semantics approach based on WOSSI provides a mapping process that could account for the many complexities that interfaces are required to adjust too such as data mapping of the associated Ontologies, taxonomy of Semantics User Interfaces and Semantic Adaptive Systems. Interfacing Semantic and Semiotic may assist when it is required for various inclusion of natural languages to adjust to the intended user but yet overcome any

Within the contextualization of Human—machine interface and supporting firmware information and knowledge there may be cognitive predisposition to the way information and knowledge may be processed within an interface C3W Kernel. If critical constructs and associated domains are therefore skew the Human—machine interfaces information and knowledge critical path outcomes might not be met and skew the Kernel (e.g. biological and

Temporal entanglement of the interface memory with information–knowledge cipher–prima strings further complicates the interface C3W Kernel. This Kernel plausibly would be

**C3**

learning delivery engines' (KILDEE's) for the user.

adverse informatics outcomes when translated for oher users.

**1. Introduction** 

engagements.

**2. Problem addressed** 

non-biological).

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