SEKE 2012 Proceedings - Knowledge Systems Institute

The rest of this paper is organized as follows: In Section
II, a case study to compare this work and previous works is
presented. In Sections III and IV, behavioral modeling and
emergent behavior detection are discus sed and ou r new
algorithm is presented. Finally, in Section V, co nclusions
and future work are given.
II.
CASE STUDY
To obtain a f air comparison between the proposed
technique and th e technique presented in [13], the same
prototype of mine sweeping robot is used. The robot's
objective is to pass 10km maze-based routes while
identifying and flagging the existing mines. The robot is not
given any map and just takes advantage of the information
received from its sensors. The robot has four sensors: a GPS
sensor that operates to co ntrol the certain distance (about
10km) robot is allowed to pass , an infrared (IR) sensor to
identify mines, a battery sensor that checks if the battery can
provide the power and an ultra-sonic (ULT) sensor which
helps the robot to n avigate the routes by finding the
obstacles. The messages from these sensors are sent to t he
client control to be processed and sent to the server control
in order to take necessary actions.
Battery
GPS IR sensor ULT sensor Client control Server control
Send signal (Battery has power )
Send signal (obstacle detected)
Motors move forward
Rotate
Stop motors
Motors move forward
Battery
GPS
IR sensor
ULT sensor
Send signal (Battery has power)
III.
Send signal (no obstacle detected)
Client control
Send signal (no mine detected)
Figure 2. MSC m2
Server control
Motors move forward
Rotate
Rotate
BEHAVIORAL MODELING
Motors move forward
Motors move forward
Rotate
Motors move
Motors move
Motors move
In this paper, the process of converting the scenarios into
the corresponding state machines based on the definitions
given in [13] is perf ormed. These definitions are t hen
applied to the case study to validate their effectiveness.
As proposed in [13], in a message sequence chart MSC,
Finite State Machines (FSMs) are built for any component i.
In Figure 3, the FSMs for the client control component in
MSC m1 ( Figure 1) are shown.
are the initial
state, the i th state and the final state of the component client
control in MSC m1, respectively.
s m1 0
Send signal (battery has power)
s m1 1
motors move forward
s m1 2
Rotate
s m1 3
Send signal( obstacle detected)
Send signal (mine detected)
Stop
Motors stop
s m1 7
stop motors
Send signal(mine detected)
s m1 6
stop
s m1 5
stop motors
s m1 4
Stop motors
Stop
Motors stop
s m1 8
Stop
s m1 f
Figure 1. MSC m1
Flag mine location
Two scenarios that represent the behavior of the robot are
presented. In Figure 1, MSC m1 shows where robot moves
since the battery has power. Later, based on the message
sent from IR sensor, the motor stops due to detection of an
obstacle. Then, ULT sensor sends a message indicating
detection of a mine but the robot has already stopped and so
the mine is just flagged.
In Figure 2, th e robot m oves since the ULT sensor,
infrared and battery sensors do not cause the robot to s top.
As in [13], messages are generalized to represent objective
instead of implementation. For example, there are t hree
messages in Figure 1, which have the same purpose of "send
signal" where the contents of the messages are different.
Figure 3. FSM for component client control of MSC m1
Then, a certain value is given to each state of the FSMs
of each component. This technique, is based o n the
definitions initially presented in [7].
Definition 1: If component i needs the result of message
for doing , then message is a semantical
cause for message and is shown by ,
where represents the j th message interacted by the
component i of MSC m.
The semantic cause is an invariant feature of the system.
As an example, in Figure 3, the message "send signal (mine
detected)" is a semantic cause for performing message
"stop". Each state of a component is defined by considering
the messages that are se mantic causes for messages that
come after it. The semantic cause is resulted from the
domain knowledge and defined as:
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