## 3 Research Results
### Experiment 3b  
Experiment 3b is an extension of experiment 2. It is aimed at examining the impact of \(AM\) technologies planned to be introduced in the company (10.1i) and the impact of key industry determinants that could influence the decision to adopt AM technology in the company (10.3i), relative to \"IF\" node. Thus, the parents for P node are \"IF\" with other nodes, P10.1 and P10.3. The validity relationships between the \"IF\" part of the network and the \"AM\" part, again refer to division according to the Pareto principle. Due to 3 components in sum, it was decided to share 40% of the weight for P10.1 and P10.3 nodes belonging to the \"AM\" network and 20% of the weight for the \"IF\" nodes. The CPT for P node is described by Equation (6). \[P_{(\text{P=State1})}=\left(0.4*\frac{\sum_{1}^{3}S_{10.1i}*w_{10.1i}}{\sum_{1}^ {3}w_{10.1i}}\right)+\left(0.4*\frac{\sum_{1}^{7}S_{10.3i}*w_{10.3i}}{\sum_{1}^ {7}w_{10.3i}}\right)+\left(0.2*\sum_{1}^{5}w_{1\text{IF}_{1}}\right), \tag{6}\] \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test No.** & **Observed Alternatives of Nodes** & \
\begin{tabular}{c} **Target P** \\ **(P = State1)** \\ \end{tabular} & **Test No.** & **Observed Alternatives of Nodes** &
\begin{tabular}{c} **Target P** \\ **(P = State1)** \\ \end{tabular} \\ \hline
0 & - & 0.04 & 13 & 9.2.1 = 0; 9.2.2 = 1; IF = C & 0.07 \\ \hline
1 & 9.2.1 = 0; 9.2.2 = 1; 9.2.3 = 1 & 0.04 & 14 & 9.2.1 = 0; 9.2.2 = 1; IF = D & 0.02 \\ \hline
2 & 9.2.1 = 0; 9.2.2 = 1 & 0.04 & 15 & 9.2.1 = 0; 9.2.2 = 1; IF = E & 0.01 \\ \hline
3 & 9.2.2 = 1 & 0.04 & 16 & 9.2.1 = 1; IF = A & 0.06 \\ \hline
4 & 9.2.1 = 1 & 0.04 & 17 & 9.2.1 = 1; IF = B & 0.05 \\ \hline
5 & 9.2.2 = 1; 9.2.3 = 0 & 0.04 & 18 & 9.2.1 = 1; IF = C & 0.07 \\ \hline
6 & 9.2.1 = 0; 9.2.2 = 1; 9.2.3 = 0 & IF = A & 0.07 & 19 & 9.2.1 = 0; IF = D & 0.02 \\ \hline
7 & 9.2.1 = 0; 9.2.2 = 1; 9.2.3 = 1; IF = B & 0.05 & 20 & 9.2.1 = 0; IF = E & 0.01 \\ \hline
8 & 9.2.1 = 0; 9.2.2 = 1; 9.2.3 = 1; IF = C & 0.07 & 21 & 9.2.1 = 0; 9.2.2 = 1; 9.2.3 = 0; P10.3 Factor & 0.15 \\ \hline
9 & 9.2.1 = 0; 9.2.2 = 1; 9.2.3 = 1; IF = D & 0.02 & 22 & 9.2.1 = 0; 9.2.2 = 1; 9.2.3 = 0; P10.3 Factor & 0.15 \\ \hline
10 & 9.2.1 = 0; 9.2.2 = 1; 9.2.3 = 1; IF = E & 0.01 & 23 & 9.2.1 = 0; 9.2.2 = 1; 9.2.3 = 0; P10.3 Factor & 0.15 \\ \hline
11 & 9.2.1 = 0; 9.2.2 = 1; IF = A & 0.06 & 24 & 9.2.1 = 0; 9.2.2 = 1; 9.2.3 = 0; P10.3 Factor & 0.15 \\ \hline
12 & 9.2.1 = 0; 9.2.2 = 1; IF = B & 0.05 & 25 & 9.2.1 = 0; 9.2.2 = 1; 9.2.3 = 0; P10.3 Factor & 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the tests for experiment 3a, estimated by the Bayesian Network 1.  
where:  
P(P = State1)--the probability of the occurrence of State 1 of \"P\" node,  
\(S_{10.1}\)--the alternative state in 10.1i nodes (State 1/0), where i {1, 2, 3: n \(\in\) N},  
\(S_{10.3}\)--the alternative state in 10.3i nodes (State 1/0), where i {1,..., 7: n \(\in\) N},  
\(W_{10.1i}\)--the weight assigned to the 10.1 nodes i {1, 2, 3: n \(\in\) N},  
\(W_{10.3i}\)--the weight assigned to the 10.3 nodes i {1,..., 7: n \(\in\) N},  
\(W_{\text{IFI}}\)--the weight assigned to the alternative i {1,..., 5: n \(\in\) N}, of \"IF\" node. In experiment 3b, there was no need to learn the \"AM\" and \"IF\" fragments of network because it consisted of previously developed parts, which were connected by relations. Occurrence P = State1, when network the resting-state network is the value 0.0424. Through experiment 3b, AM technologies planned to be introduced in the company were sought in addition the impact of key industry determinants that could influence the decision to adapt adopt AM technology in the company, with relation to trends in literature research, in order to meet the needs of companies in the context of the implementation of AM technology. The modelled network, after learning, indicates the possibility of implementing AM technology at the level of 3.91% (Table 5).