BDI AGENTS WITH FUZZY ASSOCIATIVE MEMORY FOR

VESSEL BERTHING IN CONTAINER PORTS

Prasanna Lokuge, Damminda Alahakoon

School of Business Systems, Monash University, Australia

Parakrama Dissanayake

Chairman, Sri Lanka Ports Authority, Sri Lanka,

Keywords Intelligent Agents, BDI, Beliefs, Desires, Intentions, Plans, Fuzzy associative memory, container

ports, shipping industry.

Abstract: Faster turnaround time of the vessels in berths has direct impact on the improvement of terminals

productivity. The need for an intelligent system that dynamically adapts to the changing environment is

apparent, as there is limited number of berths and resources available in container terminals for delivering

services to vessels. BDI (Beliefs, Desires and Intentions) agents are being proposed in a complex

collaborative environment in the vessel scheduling assuring better management and control in the terminal.

BDI agents to deal with many criteria and different goals with uncertain beliefs, it is proposed that fuzzy

associative memory to use in the planning process of the BDI architecture facilitating better decision

making in the whole process. In this paper we propose hybrid BDI architecture with fuzzy associative

memory in handling uncertainty issues of the vessel berthing in container terminals. Execution of Plans in a

collaborative multi agent environment would be strengthened with the introduction of fuzzy associative

memory in BDI agents.

1 INTRODUCTION

Berthing system of a container terminal requires to

determine expected berthing time (ETB), expected

completion time (ECT) of the vessels, a birth,

allocation of cranes, labour, trucks for the

stevedoring (loading and discharging) of containers

assuring maximum utilization of resources and

finally guaranteeing the high productivity of the

terminal.

Agent oriented systems are based on practical

reasoning system, which perhaps use philosophical

model of human reasoning have been used in

achieving optimal solutions for many business

application in the recent past. A number of different

approaches have emerged as candidates for the study

of agent-oriented systems [Bratman et al., 1988;

Doyle 1992; Rao and Georgeff, 1991c; Rosenschein

and Kaelbling, 1968; Shoham 1993]. The

architecture (Winikoff, 2001) has been implemented

and demonstrated the usability in number of

business systems.

BDI agent model is probably the most mature of the

intelligent agent models and has been adopted by a

few industrial applications. Berthing system in

container ports will have to satisfy various

constraints to a certain degree in making rational

decisions. In the work described, multi agent

systems model in container terminals have been

extended with the fuzzy associative memory which

greatly useful in handling uncertainty and vagueness

in the scheduling of vessels. In this paper, we

describe Hybrid BDI agent architecture coupled with

fuzzy associative memory in berth scheduling for

vessels in a container terminal.

The research is carried out at the School of

Business Systems, Monash University, Australia, in

collaboration with the Jaya Container Terminal at

the port of Colombo, Sri Lanka. The rest of the

paper is organized as follows: Section 2 provides an

introduction to berthing system in container

terminals. Section 3 describes the background of the

BDI agent model. Section 4 describes the proposed

hybrid BDI architecture for the agents in a container

terminal. Section 5 describes the schedule agent.

315

Lokuge P., Alahakoon D. and Dissanayake P. (2004).

BDI AGENTS WITH FUZZY ASSOCIATIVE MEMORY FOR VESSEL BERTHING IN CONTAINER PORTS.

In Proceedings of the Sixth International Conference on Enterprise Information Systems, pages 315-320

DOI: 10.5220/0002617403150320

 SciTePress

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 :=

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);

:= plan(B,I) /* choose plans */

while not empty (

) do

:= head(

); /* initial set of plans*/

execute(

);

:= tail(

); /* next set of plans */

get next percept p; /* observe beliefs */

B := Update(B

old

,p); /* update beliefs */

If not sound(

, I, B) then

:= plan(B,I); /* allow replan */

end-if

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(

, 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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Use of fuzzy associative memory in the BDI agent

model is described in the next section.

4.1 Fuzzy Associative Memory

Vessel scheduling in a container terminal is very

complex. This is mainly because, there are several

tasks to be executed together, uncertainty and

vagueness of the data, objectives are prioritised,

some objectives are partially satisfied etc. As with

many real life decision-making situations, it is

usually not possible to fulfil all objectives perfectly

when building berth schedules. Fuzzy associative

memory used in the BDI model essentially helps to

minimize the above constraint in vessel scheduling.

Consider the classical set A of the universe U. A

fuzzy set A is defined by a set or ordered pairs, a

binary relation,

A = {(x, µ

(x) ) | x

∈

A, µ

(x) ∈ [0,1]}, (1)

Where µ

(x) is a function called membership

function; µ

(x) specifies the grade or degree to

which element x in A belongs to the fuzzy set A.

Definition (1) associates with each element x in A a

real number µ

(x) in the interval [0,1] which is

assigned to x. Large values of µ

(x) indicate higher

degree of membership. A fuzzy rule can be defined

as a conditional statement in the form :

R1 : IF x is A

AND y is B

THEN z is C;

where x, y, z are linguistic variables; and A,B,C are

linguistic values determined by fuzzy sets on the

universe of discourses X and Y, respectively.

The proposed agent model use Mamdani fuzzy

associative memory in the schedule-agent (BDI) of

container terminal is described in the following

section.

5 THE SCHEDULE AGENT

Vessel scheduling tasks are being carried out by the

various components in the schedule-agent(SA).

Steps shown in the refined BDI execution cycle is

being followed by the agent, further SA uses fuzzy

associative memory when there are instances of data

uncertainty. The main components of the

SCHEDULE-AGENT are EVENT-HANDLER,

PLAN-SELECTOR, PLAN-MONITOR, STATIC-

FILTER, IMPACT-ANALYZER, NEGOTIATOR

and BERTH-ASSIGNER. The different components

and the proposed Neuro-BDI architecture for

SCHEDULE AGENT are shown in Figure 2.

Events are extracted from percept in EVENT-

HANDLER component and subsequently agent’s

beliefs are updated. VA may send ETA, NOB, and

LEN etc of a new vessel to SA. This triggers SA to

compute ETB for the new vessel.

Deliberation process in the PLAN-SELECTOR

component chooses intentions in achieving a desire.

Set of plans is then identified by the PLAN-

SELECTOR for execution. For e.g. SA may have

plans to check the berthing/sailing draft

requirements, and crane outreach requirements of

the berths.

PLAN-MONITOR component monitors the

execution of committed plans by the agent. if

PLAN-MONITOR ever determines that its next

level plan is no longer appropriate in order to

achieve the current intentions, then it finds an

alternative plan.

STATIC-FILTER will execute the initial set of

plans in finding out the suitable berths. IMPACT-

ANALYZER uses fuzzy associative memory in

Vessel Agents

Schedule details

Expected arrival

Request for bids/

Berth productivity

Berth Agents

Schedule Agent

Figure 1: Main Agents in the proposed vessel berthing system

BDI AGENTS WITH FUZZY ASSOCIATIVE MEMORY FOR VESSEL BERTHING IN CONTAINER PORTS

317

selecting the most efficient berth from the earlier

selected berths for the cargo operations of the new

vessel. Negotiations required to improve the berth

productivity given by BA’s will be handled by the

NEGOTIATOR component. Final berth schedule

indicating the ETB for new vessels is being assigned

by the BERTH-ASSIGNER component in the SA.

Next section describes the use of fuzzy associative

memory in IMPACT-ANALYSER component for

the selection of a suitable berth for the new vessel.

6 THE IMPACT ANALYSER

COMPONENT WITH FUZZY

ASSOCIATIVE MEMORY

The primary objective of the IMPACT-ANALYSER

is to find out a berth, which can commit the highest

productivity in serving the new vessel. Firstly, BA

requests to send the average berth productivity

(GBP

) that individual berths can commit for the

cargo operation of the new vessel. GBP of a berth i

is given as,

GBP

∑

GCP

Where, N is number of cranes used in the berth and

the gross crane productivity(GCP

) indicates the

number of moves per hour by crane i.

Operational delays (ODL

) in various berths are

considered at this point as it has a direct impact on

the completion of cargo operations in a berth.

Number of trucks (NOT

vessel

) that can be assigned

for the loading and discharge of the boxes in each

berth is also considered at this point by the SA. The

above fuzzy input parameters GBP

, ODL

and

NOT

vessel

are considered in computing the expected

vessel productivity (EVP

vessel

) of the new vessel.

Time required for the completion of cargo

operations (EOT

vessel

) of a new vessel in berth i is

then calculated.

Impact-analyser component uses three linguistic

input variables GBP

, NOT

vessel

and ODL

compute expected vessel productivity in berth i for

the new vessel, EVP

vessel

. The ranges of the

linguistic variables are defined and triangle and

trapezoid shapes are used to represent the fuzzy sets

in the proposed system. Mamdani fuzzy inference

system used by the IMPACT-ANALYSER

component is shown in Figure 3.

Linguistic values for the variables and their

notations used are described below:

GBP

= {Very-Low, Low, Rather-Low, Average,

Rather good, Good, Very Good}

NOT

vessel

= {Very-Few, Few, Rather-Few,

Average, Rather-Large, Large, Very-

Large}

ODL

= {Small, Average, Big}

Linguistic values identified for the output variable

EVP

vessel

in the fuzzy associative memory are as

follows:

EVP

vessel

= {Very-Low, Low, Rather Low,

Average, Rather-High, High, Very-High}

The knowledge based was implemented with 147

fuzzy rules. Linguistic variables and their ranges

used in the fuzzy associative memory are shown in

the following tables.

Table 1: Linguistic Variable: GBP

Value Notation Range

Very Low VL [0 –20]

Low L [15-35]

Rather-Low RL [28-45]

Average A [38-50]

Rather-Good RG [40-60]

Good G [55-85]

Very good VG [75-115]

Figure 2: Components of the Schedule-agent

Percept

Impact-analyser (Fuzzy system)

Static-filter

Event-handler Plan-selector Plan-monitor Negotiator Berth-assigner

Control Flow

Feedback Flow

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Table 2: Linguistic Variable: NOT

vessel

Value Notation Range

Very-Few VF [0 –3]

Few F [2-5]

Rather-Few RF [3-6]

Average A [4-7]

Rather-Large RL [6-9]

Large L [8-12]

Very Large VL [11-15]

Table 3: Linguistic Variable: ODL

Value Notation Range

Small S [0-5]

Average A [3-8]

Big B [7-15]

Table 4:

Linguistic Variable: EVP

vessel

Value Notation Range

Very-Low VL [0-15]

Low L [12-30]

Rather-Lowl RL [25-40]

Average A [35-50]

Rather-High RH [42-65

High H [56-90]

Very-High VH [72-110]

A sample test case scenario in a container terminal is

described in the next section.

6 A TEST CASE FOR VESSEL

BERTHING

A berthing situation at Jaya container terminal

(JCT), port of Colombo has been simulated with

BDI agents and fuzzy associative memory in BA.

JCT has four main berths: JCT1, JCT2, JCT3 and,

JCT4. Table 5 shows the berth occupancies at a

given point of time in JCT.

Table 5: Berth Occupancy in JCT

Vessels at the Terminal, time T

Beliefs Maersk ZIM APL United_V

NOB 550 525 750 490

VCR 13m 13m 18m 13m

Berth JCT1 JCT2 JCT3 JCT4

COR 13m 18m 18m 18m

ETC Sat1220 Sat0300 Sat0435 Sat0500

Declaration of a new vessel ZIM-JAPAN (ZIMJ) has

been sent by VA for scheduling. Declaration of

vessel ZIMJ minimally contains: ETA

zimj

= Sat0315,

NOB

zimj

= 1650, VCR

zimj

= 18m, etc.

Table 6 shows the inputs and final output of

expected vessel productivity (EVP

vessel

) of

individual berths. IMPACT-ANALYSER of the

SA will use the fuzzy based expert knowledge in

computing the EVP

vessel

of individual berths. Figure

4a, 4b and 4c show the decision surfaces produced

for the rule base learnt by the agent.

Table 6: Sample inputs and output value

GBP ODL NOT EVP

vessel

JCT2 62 5 4 37.7

JCT3 80 6 5 48.3

JCT4 99 5 3 64

Outputs of the EVP

vessel

received from the Fuzzy

inference system will be used to compute the

expected time required for the completion of cargo

operations (EOT

vessel

) of the new vessel ZIMJ in the

above berths. Following equation is used to compute

the EOT

vessel

EOT

vessel

With above information, BERTH-ASSIGNER will

assign a berth, which indicates the minimum EOT

for the new vessel.

7 CONCLUSIONS AND FUTURE

WORK

Paper discussed the use of BDI agents in a complex

multi agent environment in the shipping industry.

Main BDI execution cycle is refined enabling agents

to replan or to select alternative plans in achieving

its original desires or intentions. This would

essentially enhance the agent’s ability in assigning

berths for vessels in container terminals

Paper also outlined the use of fuzzy associative

memory in BDI agents, especially in dealing with

vague and uncertainty situations in the planning

stage of the vessel berthing system.

We plan to extend the research work to

incorporate fuzzy expert knowledge into the BDI

architecture, which would provide necessary

infrastructure for BDI agents to reconsider its

intentions dynamically.

NOB

vessel

EVP

vessel

BDI AGENTS WITH FUZZY ASSOCIATIVE MEMORY FOR VESSEL BERTHING IN CONTAINER PORTS

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Figure 4a: Decision surface for GBP and

NOT

Figure 4b: Decision surface for NOT and

ODL

Figure 4c: Decision surface for GBP and ODL

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