The story looks like it might generate a logic of attitudes from the logic of the contents of those attitudes.
CellNOptR uses the experimental data and a corresponding prior-knowledge network to generate a logic model and train it to maximise the fit with the experimental measurements.
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.
But then we are employing logic to derive logic from convention, generating a vicious regress.
This generates a straightforward deontic logic.
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.
Classical propositional logic is an appealing option for argumentation but the computational viability of generating an argument is an issue.
The center then selects the most appropriate route according to a route selection logic and generates a reply packet containing the selected route to be sent back to the requester.
It is found that a non-linear, artificial neural network exchange rate microstructure (hybrid) model combined with a fuzzy logic controller generates a set of trading strategies that earn a higher rate of return compared to the simple buy-and-hold strategy.
