An alternative to manual curation consists of generating a logic model from the PKN that is subsequently trained to experimental data (Saez-Rodriguez et al., 2009).
We consequently use these models to generate a Markov logic network that allows probabilistic reasoning, so that conclusions can be reached even with incomplete or partial data and observations.
The second is based on the notion of a path grammar, a form of logic grammar, to generate a path linking an ordered sequence of subcomponents.
Genetic logic circuits can be built to integrate the signals and to generate a tailored response.
This generates a straightforward deontic logic.
Also, every peripheral must have logic to generate an acknowledge signal back to the CPU.
This theory has very important philosophical consequences; for one thing, it generates a very plausible formal logic of counterfactual conditionals.
The bottom-up combination of the three operators by means of a natural trial and error logic, generates a robust and efficient optimizer, capable of competing with modern complex and computationally expensive algorithms.
But then we are employing logic to derive logic from convention, generating a vicious regress.
