![](bg2.png)
Section 6 describes a test case scenario. Future work
and conclusions are provided in Section 7.
2 VESSEL BERTHING SYSTEM IN
A CONTAINER TERMINAL
In current operations, shipping line will inform the
respective port the Expected Time of Arrival (ETA)
three months before the arrival of the ship.
Use of conventional software techniques to solve
this type of problems would cost very much for the
implementation and difficult to do so as intelligence
is required in managing the dynamic behavior of
such systems. Berthing system of a container
terminal is responsible for computing Expected time
of berth(ETB), Expected time of completion (ETC),
Expected sailing time (EST), allocation of a berth,
allocation of resources such as Cranes, Trucks, labor
etc.
3 BDI AGENTS
In the AI community the beliefs-desires-intention
(BDI) model has become to be possibly the best-
known and best-studied model(Georgeff, 1998) of
practical reasoning agents. Beliefs mean the
information about environment and can be modelled
as database records. Desires are the objectives to be
achieved by the agents. These may have different
parameters to set the priority of achieving the
objectives of the agent. Intentions are the current
selected plans for the execution Plans are used to
achieve future desires or states in the problem
domain. Agent considers many options in finally
achieving the goal set for the problem domain.
The first point to note regarding the execution
cycle given below will not observe dynamically
changing world during the execution of first set of
plans. In our Proposed hybrid BDI model for the
vessel berthing, different levels of plans are being
identified in achieving the final goals. BDI
execution cycle is given below:
Initialise-state ();
Repeat
Options:=option-generated(event-queue);
Selected-options:=deliberate(options);
Update-intentions (selected-options);
Execute ();
Get-new-external-events ();
Drop-successful-attitudes ();
Drop-impossible-attitudes ();
End repeat
4 HYBRID BDI AGENTS
Tasks involving in berths, vessels and scheduling are
being proposed to handle by three different types of
agents namely, VESSEL-AGENT(VA),
SCHEDULING-AGENT(SA) and BERTH-
AGENT(BA). Each agent handles the set of tasks
depending upon the knowledge they have and
essentially communicate and co-operate with other
agents in attaining the final desires of the system.
VA is primarily responsible for informing the vessel
details to other agents. SA schedules the vessels and
BA is responsible in assuring faster turnaround of
vessels. Main agents in the system are shown in
figure 1.
Basic control loop of the BDI is refined in
facilitating agents to capture the vessel berthing
environmental changes and allow replanning
(Wooldridge, 2000) during various stages. Refined
BDI execution cycle is shown below:
B :=
B
init
; /* initial beliefs*/
I := I
init
; /* initial intentions */
While True do
get next percept p;
B := Update(B
old
,p); /* update beliefs */
D := deliberate- options(B,I);
I := filter-options(B,D,I);
π
:= plan(B,I) /* choose plans */
while not empty (
π
) do
α
:= head(
π
); /* initial set of plans*/
execute(
α
);
π
:= tail(
π
); /* next set of plans */
get next percept p; /* observe beliefs */
B := Update(B
old
,p); /* update beliefs */
If not sound(
π
, I, B) then
π
:= plan(B,I); /* allow replan */
end-if
end-while
end-while
Where, B indicates the beliefs and B
old
means earlier
beliefs, D for desires and I for intentions. A percept
p is an input from the environment. Set of of
possible desires for the current beliefs and intentions
are being selected from the deliberate-option ( )
function. Then agent chooses between competing
alternatives, and commits to achieve them is given in
function filter-options( ) function. These chosen
options then become intentions I. Function sound(
π
, I, B ) allows agent to determine whether its earlier
plan is still appropriate in order to achieve the
current intention, if not, then it engages in further
reasoning to find an alternative plan. This implies
some (Wooldridge, 2000) degree of reactivity.
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