Dynamic Programming Structure Learning Algorithm of Bayesian Network Integrating MWST and Improved MMPC

Table 5

Discretization of the target information variables.

Node number	Target information	Discretization
1	Decision	{1,2} = { Don’t attack, attack }
2	Threat value	{1,2} = { low,medium, high }
3	Type	{1,2,3} = { jammer, bombers, fighter aircraft }
4	Intention	{1,2} = { interference, attack }
5	Capacity	{1,2,3} = { low,medium, high }
6	Importance	{1,2,3} = { low,medium, high }
7	Task point	{1,2} = { attack the machine, not attack the machine}
8	Height	{1,2} =
9	Distance	{1,2,3} =
10	Velocity	{1,2,3} =
11	Angle	{1,2,3} =
12	Radiation state	{1,2} = {close, open}
13	RCS	{1,2} = { small reflection area, large reflection area }
14	Track	{1,2} = {no,yes}
15	Warning information	{1,2} = {no,yes}