21-12-2012, 05:16 PM
APPLICATIONS OF INTELLIGENT AGENT-BASED SYSTEMS
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INTRODUCTION
Since the early eighties, Distributed Artificial Intelligence
emerged as a promising area for modelling and implementing
distributed computational processes and entities displaying
some kind of intelligence. This paper presents our perspective
of the potential application of Multi-Agent Systems together
with some ideas on the relevant research that makes those
applications possible. A more extensive reflection over the
same subject can also be found in [Oli, Fis, Ste, 99].
The structure of the article is as follows: We first give, on
chapter 1, the basic definitions both for agent and multi-agent
systems. Chapter 2 introduces the main methodologies that are
agent related (agents’ interaction, distribution and learning).
Chapter 3 puts forward several application domains and
possible solutions where agent-based systems have proved to
be useful. Chapter 4 gives a short conclusion.
Agent
The concept of agency is being now broadly used not only as a
model for computer programming units displaying a certain
kind of characteristics but also in a more abstract and general
way, as a new metaphor for the analysis, specification and
implementation of complex software systems.
Many authors have given definitions of an agent [Jen, Syc,
Woo 98]however a main distinction is that an agent, unlike
other programs, must simultaneously have at least the
following main features:
- It perceives the world where it is situated.
- It has the capability of interacting with other agents.
- It is pro-active in the sense that it may take the
initiative and persistently pursues its own goals.
Multi-Agent Systems
Although, in many cases, agents can act separately to solve a
particular problem, it often happens that a complete system
made of several different agents has to be designed to cope
with a complex problem involving either distributed data,
knowledge, or control. A multi-agent systems can therefore be
defined as a collection of, possibly heterogeneous,
computational entities, having their own problem solving
capabilities and which are able to interact among them in order
to reach an overall goal.
Multi-Agent Systems’ approach implies the use of a
methodology enabling the successful resolution of several
problems such as: the specification formalism, the
communication protocols, the agents’ coordination, the
computation efficiency, the implementation tools and the
verification methods.
We are not going to analyse all these problems in this paper.
Instead, we shall pick out some important points, which are
critical in order to make MAS useful and applicable in the
present.
There are several possible architectures for designing and
implementing agents, ranging from one extreme of simple
reactive agents [Bro 86] to the other extreme of heavily
cognitive and deliberative agents [Wit 92], [Jen, Syc, Wool 98]
]. Each one of these pure paradigms can be useful into very
distinct application domains. Robots’ teams for accomplishing
very simple missions is seen as a good example for the
application of reactive agents while co-operative expert
systems for management and control of complex scenarios
either in distributed networks or in manufacturing are seen as
good applications for cognitive agents.
AGENTS’ DISTRIBUTION AND
INTERACTION
Multi-agent Systems has been used as a framework for tackling
complex problems where task distribution is of primary
importance. According to Durfee [Dur 88], task distribution
can be recommended according to the following criteria:
- to avoid overloading of critical resources,
- to assign tasks according to appropriate agents
competencies
- to enable possible further sub-decomposition by some
agents
- to minimise communications through appropriate
clustering of agents
A variety of different mechanisms for co-ordinating agents in a
multi-agent system are already available:
- Contract Net Protocol [Dav, Smi 83] which proposes
episodic rounds of inter-communication acts
(announcements, bids, award messages). It is a simple
and widely used protocol that, on one hand, does not
affect too much the system responsiveness but, on the
other hand, neglects possible strategic reasoning
capabilities of the responding agents. The Contract
Net Protocol is mainly applicable to well-defined
coarse-grained task decomposition.
Learning
For most application tasks, even in environments appearing
simple, it is difficult (or even impossible) to determine the
behaviour of a multi-agent system a priori - that is, at the time
of its design and prior to its application. This would require, for
instance, that the designer knows in advance which
environmental requirements will occur in the future, which
agents will be available at the considered time, and plan their
interaction in response to these requirements.
Softbots
Differently to physical agents, which control a physical body in
a physical environment, like robots, softbots are software
agents living in virtual environments. They usually act as
delegates of a human or an organisation trying to fulfil their
ultimate goals (or intentions).
Information agents, in general, are computational software
systems that have access to multiple, heterogeneous and
geographically distributed information sources as in the
Internet or corporate Intranets. The main task of information
agents is to perform active searches for relevant information in
non-local domains on behalf of their users or other agents
Applications of intelligent information agents range form
relatively simple in-house information systems, through largescale
multi-database systems, to the visionary Infosphere in the
Internet. Commercial aspects of information gathering on the
Internet are becoming more and more relevant: for example,
agents may act as intermediaries supplying the relevant
information according to the users needs.
Intelligent Manufacturing Systems
The design and control of intelligent manufacturing systems is
an important goal for Multi-agent system that has deeply
influenced holonic manufacturing solutions. [Dee 94] [Fis 98]
[Bus 96]. Within a CIM system we can identify different layers
of abstraction: workflow management, shop floor control, and
autonomous control systems. Parunak presented YAMS [Van
87] as one of the first approaches models to design a flexible
manufacturing system (FMS) with a DAI approach. The main
idea of YAMS is to use the contract net protocol [Dav, Smi 83]
for task allocation in the FMS. [Ow, Smi, How 88] and [But,
Oht 92] as well as to use the contract net model for job shop
scheduling. How task allocation can be done using a reactive
scheduling approach was described in [Hah, Lev 94] [Fis 94]
and proposed a completely decentralised model for job shop
scheduling which is able to produce better results than the pure
contract net protocol because planning is done with some lookahead.
Electronic Commerce
Commerce is the way customers and suppliers meet at a certain
place and a certain time in order to announce buying and
selling intentions that eventually match and successfully start
business transactions. Due to innovations in information and
communication technologies of the past years, time and space
restrictions have been weakened and, therefore, those business
transactions became easier.
A business transaction can be defined as a set of interaction
processes between participants playing different roles -
customers, suppliers and eventually intermediaries. This
transaction is considered as complete when a trading agreement
is made between customer and supplier, and the exchange of
products or services takes place.
Entertainment
There are a large number of games available in which animated
characters face challenges in a virtual world. Fighting and
shooting games are the most prominent examples. However,
there are also adventures available in which the actions of the
characters are not so cruel. Grand and Cliff [Gra, Cli 98]
brought together agent technologies and concepts from Biology
and designed the game Creatures. Creatures provides a rich,
simulated environment containing a number of synthetic agents
that a user can interact with. Interactive theatre and cinema are
other application examples, which are particularly demanding
with respect to the abilities of the participating agents. A
number of projects have been set up to investigate the
development of such agents [Tak, Nis, Mor, Hat 97] [Hay 95] .
Traffic Management
Car Traffic management is a complex problem that can be seen
as a good example of an inherent distributed problem.
Traditional approaches to this problem usually fall into static
and/or centralised solutions.
The static solutions have the big disadvantage of being not
flexible enough to appropriately respond to frequent traffic
changes. On the other hand, reactive agents solutions have the
advantage of being totally independent and autonomous
CONCLUSIONS
Sophistication, heterogeneity, distribution and decentralisation
are characteristics of many application domains that require
appropriate and effective solutions. This paper aimed at
presenting agents and multi-agent based systems as possible
candidates for a new paradigm of designing and implementing
many of old as well as new applications seen as important in
the current information society.
Although many of the examples here referred already are in
practice, we also should stress out that several issues still
deserve more attention and further investigation is needed.
Virtual Organisations, Electronic Commerce, Entertainment
industry, Intelligent Manufacturing Systems will benefit from
future developments in the fields of automatic learning, more
cognitive agents’ architectures or enforcement of social laws.