## 1 Introduction
### The Methodology and the Possibilities of Multiple-Valued Logic Modeling  
Multiple-valued logic (MVL) [70,71] operates with logic variables, using \(K\) discrete truth levels instead of 2 ones in the Boolean logic. MVL calculus has been known for a long time [70,71,72, 72], and many of its historical aspects one can see in the collection of articles [70]. This book has presented discrete complete systems of logic operators, known as systems by [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]. Formal MVL calculus was also presented, e.g., in [70,71, 72], and discrete Allen-Givone algebra (AGA) was discussed in detail in the paper [73]. MVL discrete systems were associated with the attempts to raise the density of the processed information in chips per a logic gate [70]. Besides microelectronics, there were attempts to use discrete MVL for the realization of Kripke structures [59, 60, 61] for temporal logic and model checking tasks; however, that has proved to be too complicated for practice models. Also, MVL was used as a tool to raise the density of transferred information in laser and fiber optics lines [74]. However, more simple and accessible technologies finally have provided the priority of the Boolean logic. Nevertheless, further considered papers [68; 69; 73; 75] demonstrate many useful properties of MVL models that can be used for network agents. As AGA logic expressions and models are can be easily and conveniently calculated in parallel [75], this calculus is especially interesting for modern optoelectronic scheme design. Also, the advantage of MVL over binary is being discussed to give advantages to quantum computing [76]. Further, we do not discuss possible designs and applications of AGA logic gates; we consider only its software emulation. The intricacy here is that any logic calculus can obtain success only when combined with supporting technologies; for example, the representative graph analysis for the MVL model used in [59; 60; 61] for temporal logic modeling scarcely can be used for real multi-parametric robotic system with the large number of noise factors, probabilistic, and approximate patterns for the description of people, technical objects, and their faults. But this problem seems to be partially bypassed by the integration of traditional precise, probabilistic, and approximate calculations being held within MVL, Boolean, and fuzzy logic. One should especially note that some publications unreasonably confuse fuzzy logic and the discrete MVL, taken with the large number of truth levels. But inference rules and membership functions from fuzzy logic [75; 77] can be independently described by AGA, which also was shown to model the discrete analog of the fuzzy controller with parametric T-gates (or T-norms and T*-conorms) [77]. Regardless, all of these models were produced by the human intellect, and all their combined applications also should be modeled within AI methods. That reason explains the proposal to use the heterogeneous logic architecture of agent [75], which is the tool to simplify the joint use of various logic models in multi-parametric modeling, controlled by AGA-based schemes. The motivation to use AGA for the design of applied systems in the present paper is based on several earlier obtained results. As was discussed in [69; 73; 75], AGA gives the possibility to construct high-data-capacity multi-parametric logic functions, where for a function with \(n\) variables and \(K\) truth levels, the number of rows in the truth table enlarges as \(K^{K^{n}}\) instead of \(2^{2^{n}}\) in the Boolean model. In order to design multi-parametric data procession and verification models, the presented paper involves AGA's complete set of logic operators [70], taken with a large number of truth levels (k \(\geq\) 256) [69; 73; 75], making it especially convenient for the design of large-scale data structures in network robotics.