# Hybridization of Intelligent Solutions Architecture for Text Understanding and Text Generation
## 3 The Concept of Expounders Hybridization  
The proposed approach is based on the concept of expounder--a new pattern that encapsulates the basic functionality of text pre-processing and post-processing and can involve intelligent algorithms of text understanding. Expounder provides an adaptation of the intelligent solution. In case the existing algorithms provide low efficiency, there can be new expounders generated in manual or automatic mode to improve the logic. This process is described by expounder hybridization. Let \(A=\{a_{1},a_{2},\ldots a_{n}\}\) be a sequence of work items (jobs) that form a pipeline for processing in FIFO order. All elements of the conveyor \(a\in A\) are subject to processing by expounder \(e_{i}\in E\) with number \(i\) from the set of all available expounders \(E\) to obtain a complex solution \(S_{i}\), combined of individual working units \(s_{i}^{a}\):  
\[ \begin{array}{l}\forall a\in A\ :\ e_{i}(a)\to s_{i}^{a},\\ S_{i}=\mathop{\cup}\limits_{\forall a\in A}s_{i}^{a},\\ s_{i}^{a}=\langle NN_{j},\ DS_{k},\ CL\rangle,\end{array}  \tag{1}\]  
where  
\(NN_{j}\) is the selected neural network with number \(j\) from the set of available neural networks \(NN_{j}\ \in NN\). \(DS_{k}\) is the selected dataset with number \(k\) for training the neural network from the set of all available datasets, \(DS_{k}\ \in DS\). \(CL\) is a list of commands, which is generated depending on the selected neural network and dataset: \(CL=f:\ NN_{j},\ DS_{k}\ \rightarrow\ \{C_{1},\ C_{2},\ \ldots,\ C_{m}\}\), in which \(C_{i}\) is one atomic instruction. In the case when processing of some job \(a\in A\) is impossible, \(s_{i}^{a}=\varnothing\), the expounder \(e_{i}\) is replaced with the next one in order \(e_{i+1}\in E\), and then the processing process (1) is repeated. In the case when several expounders are able to fulfill the job \(A\) and start competition for it, the system performs an auction, which allows the identification of the winner with the best solution (2). For this, the evaluation function \(f\) is introduced and the time period \(\tau\) is designated, during which the evaluation of the expounder \(e_{i}\) for the performance of \(A\) is valid:  
\[W_{e_{i}}^{\tau}(A)=\frac{1}{n}\sum_{a\in A}f([e_{i}(a)]), \tag{2}\]  
where \(W_{e_{i}}^{\tau}(A)\) is the value of expounder \(e_{i}\) performing job \(A\), valid for time \(\tau\), and \(n\) is the number of units \(a_{i}\) in job \(A\). The estimate \(W_{e_{i}}\) remains valid during the interval \(\tau\), and must be recalculated after it expires. The implementation of such a restriction makes it possible to ensure the stable structure of the expounder and guarantee the result only for a given period of time while opening up opportunities for the development of the expounder if the decision to use it in this task is delayed for external reasons that inevitably arise when working in a multi-user, multi-threaded mode with a shared resource in a single information space. The function of estimating \(f(a)\) in (2) depends on the class of problems to be solved, e.g., it can be the probability of information recognition, accuracy (value, inverse error) or the time of operations execution. In situations where none of the expounders \(e\in E\) can finalize the entire job \(A\), it requires hybridization. For this purpose, we represent each expounder \(e\in E\) by a set of pairs of neural network \(NN\) and dataset \(DS\):  
\[e=\{\langle,NN_{1},DS_{1}\rangle,\langle NN_{2},DS_{2}\rangle,\ \ldots,\ \langle NN_{m},DS_{m}\rangle\} \tag{3}\]  
Hybridization is carried out in one of the following ways (see Figure 1):  
* Transition--replacement of one ineffective expounder element with an effective expounder element, for example, one neural network \(e_{i}:NN_{i}^{{}^{\prime}}\) with another \(e_{j}:NN_{j}^{{}^{\prime\prime}}\) while saving the dataset. The borrowed element is replaced by another: \(e_{i}:NN_{i}^{{}^{\prime}}\leftrightarrow e_{j}:NN_{j}^{{}^{\prime\prime}}\).