COORDINATION IN MULTI-AGENT DECISION

SUPPORT SYSTEM

Application to a Boiler Combustion Management System (GLZ)

Noria Taghezout

, Abdelkader Adla

and Pascale Zaraté

Department of Computer Science, University of Es-Senia Oran, BP 1524, El-M' Naouer, 31000, Oran, Algeria

IRIT, INPT – ENSIACET, 118 route de Narbonne 31062 Toulouse Cedex 9, France

Keywords: Multi-agent system, Group Decision Support System (GDSS), MADS (Multi-agent Decision support

system), Coordination agent.

Abstract: In Multi-agents systems, the cognitive capability present in an agent can be deployed to realize effective

problem-solving by the combined effort of the system and the user. It offers the potential to automate a far

wider part of the problem solving task than was possible with classical DSS. In this paper, we propose to

integrate agents in a group decision support system. The resulting system, MADS is designed to support

operators during contingencies. We experiment our system on a case of boiler breakdown to detect a

functioning defect of the boiler (GLZ: Gas Liquefying Zone) to diagnose the defect and to suggest one or

several appropriate cure actions. In MADS the communication support enhances communication and

coordination capabilities of participants. A simple scenario is given, to illustrate the feasibility of the

proposal.

1 INTRODUCTION

In Distributed Artificial Intelligence (DAI) systems,

problem solving agents cooperate to achieve the

goals of the individuals and of the system as a

whole. Each individual is capable of a range of

identifiable problem solving activities, has its own

aims and objectives and can communicate with

others (Jennings, 1993). Typically agents within a

given system have problem solving expertise which

is related, but distinct, and which has to be

coordinated when solving problems. Such

interactions are needed because of the dependencies

between agents’ actions, the necessity to meet global

constraints and because often no one individual has

sufficient competence to solve the entire problem.

In this paper, we propose to integrate agents in a

group decision support system. The use and the

integration of software agents in the decision support

systems provide an automated, cost-effective means

for making decisions. The agents in the system

autonomously plan and pursue their actions and sub-

goals to cooperate, coordinate, and negotiate with

others, and to respond flexibly and intelligently to

dynamic and unpredictable situations.

We argue that an agent-oriented approach is the

most natural and appropriate mean to achieve better

support for the group decisions, and we propose to

improve the coordination protocol.

The manufacturing process of the oil plant

(GLZ), selected as application domain in this study,

is split into two subdivisions: Utility subdivision and

Process subdivision. The Utility subdivision is

constituted of Pumps, Desalination Unit, boilers,

Turbo-generators and Air Compressors while the

Process subdivision concerns the tasks of

manufacturing of liquefied Gas. This subdivision is

composed of 6 strings where a string is group of

equipments. Every string contains 10 sections which

are going to be used to liquefy gas. The management

system of the boiler combustion is one of the most

critical systems for the good functioning of the plant

and has a high impact on the methods of cogitation

and apprehension of various problems related to

maintenance (see Figure 2). The exploiting staff is

often confronted with situations that impose a quick

reaction of decision-making. This requires

consequent human and material resources and

adapted skills (for more details see (Adla, 2007)), to

diagnose the defect and to suggest one or several

194

Taghezout N., Adla A. and Zaraté P. (2009).

COORDINATION IN MULTI-AGENT DECISION SUPPORT SYSTEM - Application to a Boiler Combustion Management System (GLZ).

In Proceedings of the 11th International Conference on Enterprise Information Systems - Artiﬁcial Intelligence and Decision Support Systems, pages

194-199

DOI: 10.5220/0001999301940199

 SciTePress

appropriate cure actions.

The reminder of the paper is organized as

follows: Section 2 describes our contribution. It is

followed by section 3 which covers the integration

of agent technology into a DSS. The multi-agent

architecture for group decision support systems and

the corresponding coordination protocol are

described in section 4 and section 5. We also present

an example of a scenario in section 6. Finally,

Section 7 gives some concluding remarks.

2 CONTRIBUTION

Research that studied group decision support

systems in the existing literature used mainly face-

to-face facilitated GDSS. Some of its results may not

apply to distributed teams (Chen, 2002) that, it is

difficult for distributed teams to arrange face-to-face

meetings or to meet at the same time virtually.

Moreover, although most presented GDSS

environments try to solve problems in the real world,

the lack of an integrated procedure, from decision

identification, basic information acquiring, to final

decision proposed, makes the systems only partially

supportive or even needful of outside assistance.

Still, despite the existence as well as the extensive

use of numerous general-purpose commercial

systems, it is our belief that these systems do not

readily fulfill the needs or operational usages of

specialists or experts in different organizations to

render their expertise in GDM processes.

In our study we consider another gap: the

coordination problems when they occur have several

causes. Most of them are a consequence of

limitations in both the decision making processes

and the technological support for communication.

For this reason, the information and tasks related to

the decisions made in GDDS have to be visible to

other organizations to keep the relief effort

coordinated between the agents.

In addition, the quality of support received

during the decision making processes is the key to

reaching optimal decisions. Decisional guidance

mechanism provides the decision makers with step-

by-step guidance throughout the decision-making

process and allows them to evaluate more

alternatives. As a result, DSS users with decisional

guidance can easily come up with better decisions

than those with no decisional guidance.

Mahoney et al. (Mahoney, 2003) pointed out that

when faced with Complexities in a decision

situation, decisional guidance helps users to choose

among and interact with a system’s capabilities.

They argued that in less structured tasks that deal

with uncertainty and risk, users need more guidance

to choose among competing solution techniques or

among alternative methods of processing

information to structure an appropriate decision-

making process using the GDSS.

3 AGENT INTEGRATION IN DSS

We got inspired by two main research works.

Firstly, the main ideas resumed in the table

described in (Forth, 2006) were very interesting for

our study (see Table 1). It defines how the capability

of an agent may be utilised in a DSS application, and

also identifies alternative agent design architectures

suitable to underpin this. As a constituent part of

problem-solving in the domain, an agent may choose

particular sources of information to use. Data

Gathering may be a function within an agent

(sensing), or a dedicated activity of a specialised

information agent if the task is complex.

Secondly, the approach developed by Zamfirescu

(Zamfirescu, 2003) addressed the problem of self-

facilitation in GDSS by establishing a common and

meaningful high-level collaboration pattern among

the group members inspired from the SP theory. In

his GDSS approach, the main entities have been

defined: the personal assistant agents, the resource

agents and the plan agents.

Table 1: Mapping DSS functions to agent capabilities.

DSS Function Agent Function

Data collection

Knowledge acquisition

and assimilation

Model creation

Perception and knowledge

representation

Alternatives case

creation

Planning and reactivity

Choice Action selection

Implementation Action execution

4 THE MULTI-AGENT SYSTEM

Agents are used to collect information outside of the

organisation and to generate decision-making

alternatives that would allow the user to focus on

solutions that were found to be significant.

According to this a set of agents is integrated to the

system and placed in the DSS components (as shown

in Figure 1), we distinguish:

COORDINATION IN MULTI-AGENT DECISION SUPPORT SYSTEM - Application to a Boiler Combustion

Management System (GLZ)

195

Figure 1: Agent architecture for Individual DSS (Adla et al., 2007; Jennings, 1993).

Figure 2: A partial hierarchy of tasks and methods of the application ( A01, A05 , SD1 and A12: feasible methods;

Decompose1, 2: Decomposition methods).

The Interface Agent (IA) Continuously receives

data from the process – e.g. alarm messages about

unusual events and status information about the

process components.

A Decision Maker Agent (DMA) performs most

of the autonomous problem solving.

An Information Retrieval Agent (IRA)

primarily provides intelligent information services.

A Diagnosis Agent (DA) is activated by the

receipt of information from DMA which indicates

that there might be a fault.

Knowledge Management Agent (KMA)

comprises, manage and update knowledge base;

The Action Agent (AA) generates a plan of

action which can be used to repair the process once

the cause and location of the fault have been

determined. As described in Figure 3, a refined

representation of DA, AA and KMA agents is given.

The Coordinator Agent provides two services to

task agents: (i) it computes summary information for

hierarchical plans submitted by the task agents, and,

(ii) coordinates hierarchical plans using summary

information.

5 A COORDINATION

PROTOCOL

The problem solving mechanism is based on a set of

cycles until the entire problem is solved. Each cycle

consists of the following steps :(1) identifying

candidate methods; (2) identifying triggered

methods ;( 3) selecting a method; (4)assigning the

method to an agent; (5) executing the method; and

(6) Evaluating the task state.

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5.1 Agents Structure

Clearly, all the modules representing the inner

structure of an agent may depend on each other.

This is especially true for the local problem solver

and the coordination module (as shown in Figure 3.)

which do not only exchange real time information

but, in addition, must coordinate their decision rules

and performance criteria. If we consider the

relationship between the coordination module, the

problem solver, and the knowledge base. We found

that they have to make sure the data needed

available. The communication between the agents

may roughly be described by the coordination

module and the interface component. They are

describing the way how agents may communicate.

5.2 Agent Communication Language

The agents needs are formalized as a set R of

requests r

∈

R, each of which expresses a question

about agent’s data or an action to perform given a

set of parameters. These requests appear both in

human-agent and in interagent dialogues.

The request performative rp can take the following

values, for example see Figure 4, Figure 5 and

Figure 6):

• Ask-for requests that represent questions about

the agent’s data.

• Assert-is for assertions about agent’s data values

(to answer to Ask-for requests).

• Order for requests that represent actions to

perform.

• Affirm for acknowledgment when an action has

been performed (in answer to an Order request).

• Assert-cannot to express that the agent is unable

to perform a command.

• Assert-can to express that the agent can perform

a command, but misses a field value to be able

to do so.

• Unknown for asserting that the agent doesn’t

know a field or the overall action to perform.

5.3 Communication between Agents

The requests structure presented above is also used

by agents in their communications. Indeed, these

requests formalize the contents of the messages

exchanged by agents, each of which is structured as

follows: m = [id, C, sender, receiver, agenda].

Where id is the message id, C is the message

content, sender and receiver are the sender and

receiver agents’ ids and the agenda term for

indicating the memory of the message associated to

the initial agent request.

Figure 3: Representation of DA, AA, and KMA agents.

5.4 Discussion

When an agent receives:

 An Order request, and if it misses a value to

perform it, it builds an Assert-can answer.

 An Assert-can request, it replaced the required

field stated in the assert-can answer by its value

if owned.

 An unknown request, it looks in the

corresponding field in the Ask-for request.

 An assert-is request, when answering an Ask-for

request, it can trigger it by using the information

received trough the Assert-is answer.

Figure 4: Ask-for protocol.

Coordination

module

Knowledge

Base

Problem-solver

Interface

COORDINATION IN MULTI-AGENT DECISION SUPPORT SYSTEM - Application to a Boiler Combustion

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Figure 5: Unknown protocol.

Figure 6: Order protocol (fr: final response).

Figure 7: A coordination scenario diagram (AUML).

6 A COORDINATION SCENARIO

When the task management agent (DMA) receives a

task from an interface agent (IA), it decomposes the

task based on the domain knowledge it has and then

delegates the primitive tasks to the other agents

(IRA, MA, KMA, DA or AA). The task

management agent will take responsibility for

retrieving data, modelling, diagnosing fault,

planning action, resolving conflicts, coordinating

among the related agents and finally reporting to the

interface agent which conveys the results to the user.

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As described in Figure 7, the DMA agent first

gets input data through the interface agent. Next, the

modelling agent searches for rules to select a

suitable model and to execute the model to get

analytical results. Additionally, all the parameters

values needed by the models are retrieved from the

database via the information retrieval agent. After

finishing model analysis, the diagnosing and the

action agents use the results of the model analysis to

identify the fault causes and to perform a suggested

action plan.

7 CONCLUSIONS

MAS paradigm offers a new dimension with respect

to GDSS integration with complementary services,

making it easier to build complex and flexible

architectures suitable to organizational settings

(Zamfirescu, 2003).

In this paper, we have integrated agents into

GDSS for the purpose of automating more tasks for

the decision maker, enabling more indirect

management, and requiring less direct manipulation

of the DSS. In particular, agents were used to collect

information and generate alternatives that would

allow the user to focus on solutions found to be

significant. We expect to finish the system

implementation that supposes all the decisional tools

and validation with other oil plants which are

geographically dispersed.

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Chen, M., 2002. TeamSpirit: Design, implementation, and

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