vironment | 
  
  
  
  
)bservation 
tion” 
eration 
er 
| 
n making in agent 
dentify its nearby linear 
functionalities of such an 
observes and senses its 
nates (x, y) of all objects 
coordinates of endpoints 
[n this case, the agent is a 
ects around the agent are 
ines, and polygons. The 
)bject is calculated based 
esearchers estimated the 
d on the distance of their 
This study constraints its 
using the combination of 
'scribed in the following 
agent toward the line is 
  
> 
nt B I 
E (1) 
D, 
  
ons (x and y) of the agent 
/. This angle is shown in 
M lal B 
Line 
fan agent 
ing the same see angle to 
n in Figure 4. Therefore, 
entify the specific object 
lated to the line is the 
ed data. The direction is 
3 
or Dy ) Q) 
Where: P 4,4, PA and Pg are the positions (x, y) of the agent and 
both ends of the line respectively. 
  
Figure 4. The locus of all points in the area having the same see 
angle to the line 
For many proximity analysis experimented by authors, a "see 
angle" is enough for identifying the closest object. Based on the 
see angle, two cases might occur: 
1, “see angle” is less than 90° : In this situation, 
two sub-cases happen: 
a. The agent is near the line, but it is located 
out of the space between line endpoints 
(e.g. point number 1 in Figure 4). 
b. The agent is near to the line; such as point 2 
and 3 in Figure 4. 
2 “see angle” is more than 90°: The point is 
located near the line; as a result, the “see angle” is 
close to 180? (e.g. the see angle of point B shown in 
Figure 4). 
The last parameter is the length of the line observed by the 
agent. If the line is long and far away from the agent, it has a 
negative effect on the "see angle. So the length of the lines must 
be considered as well. 
3.3 Agent's Knowledge Base 
Agent can make a decision of which object is in its proximity 
area based on the rules in agent's knowledge base. These rules 
are based on the see angle (Eq.1) and the sign of the direction of 
point (Eq.2). This knowledge is considered for all objects 
around the agent. These rules are as: 
Rule (1): The direction of point to the line (D) is evaluated. If 
the sign D is negative, it shows that the agent is not in the area 
between two ends of the line, so it must be deleted; otherwise, if 
the sign is positive, it shows that the agent is in the area 
between the two ends of the line. 
Rule (2): If the “see angle” is more than 90°, the objects are 
selected. For these objects another factor is calculated. The 
factor shows the approximate distance of the agent to the object. 
The factor is estimated as: 
Scu fr 
2tan(@/) 
Where: a. is the "see angle" of agent to the object and L is the 
length of the line. 
Figure 5 shows the flowchart of the proposed agent for 
extracting linear objects. In reality, It is rare that more than one 
line with “see angle” greater than 90°; if so, the algorithm needs 
to use an approximate distance (S) to filter the reminding 
points. 
(3) 
  
(X, Y) of all lines (X, Y) of the Agent 
endpoints 
Delete the line 
Yes 
  
  
    
       
       
Calculate: 
œ: “see angle” 
D: sign of agent 
  
   
Exit one 
object in 
area? 
Figure5. The methodology used for object selection 
4. IMPLEMENTATION AND DISCUSSIONS 
The proposed agent is implemented on the sample data as 
shown in Figure 6. The test area contains 11 lines with various 
directions and lengths around the point object (agent) The 
question is to identify and select the closest line to the agent. At 
first, the see angle and the D parameters is calculated for each 
line. Next, the nearest object to the agent is selected based on 
the rules of the agent. 
  
Figure 6. The experimental data contain 11 lines and one point 
As seen the result of the algorithm, the nearest line is line 
number 5. Similarly, Table 1 shows that line number 5 and 11 
have the highest see angles. Once the approximate distances are 
calculated, it shows that line number 5 is the one which has all 
conditions. It must be mentioned that the selected line is not 
mathematically the nearest one to the point; as seen in Table |, 
the nearest line to the point is the line number 7, but it is not 
selected by the agent. 
To assess the efficiency of the proposed extraction operator, the 
common and the agent-based selection operator are 
implemented on a real urban map of Tehran (Iran). The process 
is done to update the traffic geospatial data base. In this 
problem, the connection between house and road is determined. 
In this case, it must be defined which road is related to which 
house. As a result, the ID of the road is stored in the house 
layer. The problem is to select the nearest line (road) for the 
identified points (houses). At first, the problem is solved by