**9. Simple causality mapping**

254 Semantics – Advances in Theories and Mathematical Models

Within the dynamics of deformation and flow of matter (Rheology), biological logic gates (B–gates) with bio-delivery engines might be the reality for Human—machine 'Worldwide Interoperability' communication–conversation. Like in human communications there are misunderstanding (random radicals) that are possible within the supporting spectrums and

Augmentation of host's and systems may focus on intellectual effectiveness, wisdom extraction and assimulation-adaptation to the users needs (*Guanxi type C3W*). Use of *Guanxi type C3W* personalised command, control and communication (*C3*) networks of influence could translate into impairment correction and enhancement Human-machine interfaces.

In the context of mapping Artifical Intelligence (AI) and Artifical Wisdom Intelligence (AWI) appears to require development of a Semantic–Semiotic based control-language vocabulary and sybolism to translate into impairment correction to augment interoperability between biological and non-biological interface communication–conversation. This therefore suggests alternatives to augment both the biological host and non-biological AI (e.g. symbolic, connectionist; situated activity). As both the host and AI are meshing and merging processes, there could be cognitive science implications that suggests the likely existance of AI impairment that may be random radicals that adversely impact on achieving correction

Augmented Reality (AR) together with AI within a host (entity) may result in compounding error correction (e.g. biological host and non-biological AI Adaptive Intent-Based Augmentation System that have contra wisdom intends) with unintentional consequences. Such a augmented wisdom entity may have unique Biorheology interface entangled single– to–multiple chain KILD's for a *Guanxi type C3W* personalised command, control and communication (*C3*) networks access between Human—machine 'Artifical life derived entity replication' (ALDER) which may be user modified ('Artifical life guided entity replication' (ALGER). Artificial life that is being suggested to be the bases to Human-machine interfaces tend to involve integrates motor, perceptual, behavioural, and cognitive components

Depending on the user and systems shared understand and knowledge the communicative mechanism and intent may need C5M with SIAN to lever the required level of AR application augmentation. This augmentation may suggest need for decision caution, need for diagnosis and intervention or entity re-purposing (AIBAS, 2006; Adkins et al., 2003).

Via Informatics Medicine (definition of Informatics medicine refers to using informatics for medicinal purposes) AWI's coulds be could be the leap beyond 'virtual entity—person in a reality environment' (Avatar). A version may be the 'artificial life derived entity replication' (ALDER) to 'artificial life guided entity replication' (ALGER). The may assist by augmentation of 'human like operations' to overcome impairment or enhance capabilities.

Various logic delivery engines requires complexities in the supporting knowledge information—learning. What may be an outcome are false negatives, confusion, or skewed

bandwidths as they exist over multiple hybrid access interfaces.

The enablers are likely to be C3W Informatics Semantic temporal mapping.

and enhancement of Human-machine interfaces (Engelbart, 1968; Spector, 2001).

**7. Augmentation of systems** 

(Terzopoulos, 2009).

**8. Delivery engines** 

interpretation (Watkins, 2000; WSU, 2003).

An initial simple mapping tool is required to validate 'proof—of—concepts' before moving into complex mathematics and Truth Tables. A simple mapping tool facilitates adjustment of support knowledge—information—learning that might span discrete and possibly dilated entities, events and relationships in time, space, place, and tempo.

This paper therefore, put forward a plausible paradigm shift using wisdom-based open– system mapping of causality. By doing so, the blocks to digital logic circuits are broadened to provide nexus wisdom based 'Causality Logic gates' (COR gates; Fig 4). These gates are able to provide SIANS (synergy, integration, assimilation narrative, and synchronization) for clone strands to or with other hierarchies and hybrid gates to enable an evolutionary jump(s)2.

Fig. 4. Concept of WOSSI nexus 'Causality Logic gates' (COR gates) (Wander et al., 2004; Roh, 2006).

 2 Wave revolutions might be to be: First (agricultural); Second (industrial); Third (information); Fourth (overload–over–choice); Fifth (Quantum human–machines); Sixth (Unity); Seventh (Merged society?) (Dictionary, 2008; Golec, 2004).

C3

**12. Semantic mapping** 

Control\_semantics\_promise

(Coppock, 2005)

Meta\_semantic [Ikehara, et al., 2007)

al., 2006)

Control\_semantics (Kiss, 2005)

Coordination \_semantic (Armarsdottir, et al., 2006)

 Domain: "*entities*" Model: "*representation*"

et al., 2005; Avery et al., 2004).

within key themes (Table 2) for a given scenario—context.

**Themes Key details /Constructs** 

In summary, Semantic mapping comprises:

Ontology: "describes/ description" taxonomies)"

W semantic Temporal Entanglement Modelling for Human - Machine Interfaces 257

Semantic mapping (Fig 5) has variability within a 'fit–for–purpose' ethos (e.g. F–semantics; I–semantics). 'Fit–for–purpose' results in the need for cross platform control systems and memory with information–knowledge cipher–prima strings that may support families of delivery engines. Semantic mapping aids in verification, interoperability and collaborative distribution and facilitates moving from the macro, meso, micro, and quantum–nano scales

> Logical semantic category; Truth items (Common Concepts);

model-Reference Ontology;

Mapping;

Proposed a responsibility-based for control "shift" phenomena.

Contrast view theory of control and non-finite complementation.

Semantically equivalent mapping: Truth Items.

Processes solve the interoperability emerges;

Proposes a layered approach in the system.

Table 2. Semantic Themes (Coppock, 2005; Kiss, 2005; Ikehara, et al., 2007; Armarsdottir, et

Fig. 5. Semantics modeling—dynamics mapping (He et al., 2005; Hatten et al., 1997; Wagner
