Sentence examples for representation of rules from inspiring English sources

Shastri, V. Ajjanagadde, From simple associations to systematic reasoning: a connectionist representation of rules, variables, and dynamic bindings using temporal synchrony, Behav.

The third is the explicitly unique representation of rules in the same equivalence class (mathrm{AR})(L, S) upon the generators and their closures, (L, ({mathcal {G}}) (L)) and (S, ({mathcal {G}}) (S)).

To completely eliminate the generation of duplicate candidates for the solution, based on each class ((L, S) in mathrm{NFCS}_{supseteq L_0, supseteq R_0} (s_0,s_1,c_0,c_1 )) and the generators ({mathcal {G}})(L), ({mathcal {G}})(S), we will propose an explicitly unique representation of rules in (mathrm{AR}_{supseteq L_0, supseteq R_0}^+ (L,S)).

Also, GDL seems just as suitable to implement CDS rules to be used at the point of care as it is to implement CDS rules for retrospective compliance checking; the representation of rules in GDL does not differ for those two use cases.

Since this paper focuses on the area of data retrieval, in the following, not all operators that can be used for the representation of rule knowledge, are described in detail.

Open image in new window Fig. 3 Representation of rule base of FIS1 Model Open image in new window Fig. 4 Surface views of FIS1 model a FIS1 vs. dissolved solids and alkalinity b FIS1 vs. hardness and alkalinity c FIS1 vs. pH and alkalinity d FIS1 vs. pH and hardness e FIS1 vs. dissolved solids and hardness f FIS1 vs. pH dissolved solids.

Open image in new window Fig. 7 Representation of rule base of FIS2 Model Open image in new window Fig. 8 Surface views of FIS3 model a FIS3 vs. Nitrate and Sulphate b FIS3 vs. Fluoride and Sulphate c FIS3 vs. Fluoride and Nitrate.

Open image in new window Fig. 5 Representation of rule base of FIS2 Model Open image in new window Fig. 6 Surface views of FIS2 model a FIS2 vs. Mg and Ca b FIS2 vs. Fe and Mg c FIS2 vs. As and Mg d FIS2 vs. As and Fe.

Open image in new window Fig. 9 Representation of rule base of FWQI Model Open image in new window Fig. 10 Surface views of FWQI model a FWQI vs. FIS1 and FIS2 b FWQI vs. FIS3 and FIS1 c FWQI vs. FIS3 and FIS2.

VLPFC is densely connected with inferior temporal cortical areas involved in processing of information about visual objects (Webster et al., 1994; Petrides & Pandya, 2002), and is associated both with the representation of rule information associated with visual cues (Bunge et al., 2003) and with the storage of sequences of information in working memory (Owen et al., 1999).

However, today at Partners, the collaboration environment is not integrated with the RAEs, so documents and specifications vetted by SMEs (e.g., free-text or intermediate representation of rule logic) are stored, maintained and shared separately from the RAE as Microsoft Word or Excel documents.