# Multiple-Valued Logic Modelling for Agents Controlled via Optical Networks
## 3 Results
### Representation of Quantum Protocol by AGA Function
This can be disclosed by Equation (11), where all variables tagged by \"-\" in Table 5 are omitted:  
\[\begin{array}{rl}F_{VP}=&C_{1}\;*\;X_{t}(t_{1},t_{1})\;*\;X_{P}(p,p)\;*\;X_{ jp}\big{(}j_{p1},j_{p1}\big{)}+\\  
The other 20 product terms also should be appropriately rewritten. After the design and clarification of the involved hardware components, one should specify the appropriate noise parameters and reduce the band of indexed parameter \(b\) in Literals with \((0,\,k-1)\) from \(k-1\) to some determined by noize value \(\overline{b}\). In other words, function \(y\ =\ F_{VP}\) given by Equation (12) should be redefined after clarification of its noise parameters. Namely, this procedure will be correct, as there are no formal reasons to mix 0 and \(k-1\) constants in one logic expression, and this should be carried out by hand in the final truth table for the new function \(y\ =\ F_{VP}\). Using the procedures discussed above, one can write the function, describing the interaction of prover and verifier agents, into the ledger function as a reference procedure for tests or restoration. Position-based verification with protocol PU [33] here is not the obstacle, and both quantum and classical procedures can be united in the AGA logic model. One more substantial note should be given, referring the further necessity to consider the model of trust for the interaction between the prover and the verifier. The joint logic function \(y\ =\ F_{VP}\) discussed above has included the prover and the verifier as two different agents, according to their signals used in the verification protocol. However, their real interaction will also depend on the honest and dishonest behavior of the prover. In order to obtain a more detailed and realistic description of the verification procedure, it is further necessary to add a model of trust for their interaction. Another interesting aspect of a fiber optics network segment is that advances in this field create the possibility to complement the PBC task and its more secured but specific PU by a less protected but more massive QKD protocol for the delivery of confidential digital data, such as photos, from the neural network database. At least, the recent research of the continuous-variable QKD scheme [84] can potentially be used for the design of \(k\)-valued truth models of AGA. Moreover, this paper discusses the trusted component of the phase noise model, so that the agent model for such hardware schemes can directly include several different noise models into the control scheme of the agent. That means there is a necessity to design more complicated models of trust, directly including physical parameters into agent models. It seems that multiparametrical models of AGA can be quite appropriate for the integration of such physical processes. The frequency polarization multiplexing system used in [85] additionally enlarges the interest in the continuous-variable QKD scheme. Besides this, the equalization enhanced phase noise [86] is attributed to wavelength division multiplexing schemes of fiber optics, which seems to be the most reliable scheme for the realization of multi-level logic. Moreover, the phase-coded noise compensation tools in QKD schemes [86] are attributed to dispersion compensating fiber, chirped Bragg gratings, dispersion-shifted fiber, and reduction of the chromatic dispersion effect by optimal initial pulse duration. All these schemes can potentially be considered as components for AI control agents, but the agent's architecture and MVL model can differ substantially for them.