An Agent Future For Network Control

Intelligent Agents
INFORMATIK • INFORMATIQUE 1/2000 25
An Agent Future for Network Control?
Steven Willmott, Monique Calisti
In an age of rapidly increasing network complexity and diversity, the idea of “bringing intelligence to the
network” is becoming more of a necessity than “future work”. Since the early 1990’s, agent technology has
often been proposed as a way of achieving this more effective, robust and above all autonomous network con-
trol. This article provides a brief tour of current trends in network development and discusses the potential
for agent based solutions to some of the most pressing communications network problems.
Introduction
From a network engineer’s point of view, a network is a
complex system requiring complicated management under
very trying domain constraints. To a DistributedArtificial Intel-
ligence researcher, a network is a highly distributed, complex
and challenging environment for the application of intelligent
systems ([Lewis95], [Maes94], [Weihmayer/Velthuijsen 94]).
The idea of distributing communication network control and
management tasks by deploying “smart”, “cooperative” and
“autonomous” entities in network infrastructures has thus
received considerable attention from both the Distributed
Artificial Intelligence (DAI) and the Communications Network
(CN) communities.
As networks become increasingly complex and difficult to
control, the ideal of a distributed, intelligent network manage-
ment and control system is becoming more and more of a
necessity. Furthermore, new software and network technolo-
gies are revolutionising what can be deployed in the network
and even what we think of as the network itself. Despite the
lack of deployed systems, these trends make an “agent future
for network management” seem closer than ever.
This article does not aim to replicate the useful surveys
already completed in this area. Instead, the aim is to give a
briefer overview of the research field which balances the tradi-
tionally separated CN and DAI viewpoints. Rather than going
into detail on individual research efforts we review the area by:
• Identifying the current trends which suggest that agent tech-
nology may play an increasingly important role in network
control (Section 2).
• Highlighting three key areas which might benefit most from
agent technology: multi provider environments (Section
3.1), resource management (Section 3.2) and communica-
tions integration (Section 3.3).
• Discussing the necessary steps for the deployment of agent
systems in future communications networks (Section 4.).
Those readers interested in more detailed accounts of
previous work should find the following surveys useful starting
points:
• [Kumar/Venkataram 97], [Weihmayer/Velthuijsen 98] and a
recent volume of collected works [Hayzelden/Bigham 99]
all give useful DAI perspectives.
• [Martin-Flatin/Znaty 2000] gives an overview of existing
network management paradigms which places work on
agents in a Network Management context. More specific
works on software agents for management operations can be
found in previous proceedings of the IATA1
and DSOM2
workshops.
1.1. “Agent” Terminology
One of the unfortunate side effects of the separation of work
between the DAI and CN communities is confusion over
terminology – particularly surrounding the term “agent”. Many
similar terms (for example SNMP agents, mobile agents,
“intelligent” agents, agents, BDI agents) are used for different
purposes by the two communities. In this article we follow the
agent definition given in [Jennings/Wooldrige 98]. This defini-
1
1. Intelligent Agents for Telecommunications Applications.
2. Distributed Systems: Operations & Management.
Steven Willmott is a researcher in the Artificial Intelligence
Laboratory at EPFL Lausanne. His primary research interests
centre on the control of distributed systems using Distributed Ar-
tificial Intelligence techniques – with particular focus on agent co-
ordination, organisation and language. In 1999 he chaired the 3rd
workshop on Artificial Intelligence in Distributed Information
Networking (AiDIN’99 held at AAAI in Orlando, USA). Aside
from his academic activities he is also heavily involved in the
FIPA agent standardisation effort as the editor of the 1999 FIPA
Agent Message Transport Specification.
Monique Calisti has received her master degree in Electrical
Engineering at the University of Bologna (Italy). After having re-
ceived the pre-doctoral School diploma in Communication Sys-
tems at EPFL, she joined the Artificial Intelligence Laboratory of
EPFL at the end of 1997. She is currently PhD student and her pri-
mary area of interest is the interaction of distinct telecommunica-
tion network providers. The focus is on the allocation of multi-
provider service demands with Quality of Service requirements.
The use of Distributed Artificial Intelligence techniques for ena-
bling a flexible and automatic provider-to-provider paradigm rep-
resents one of the main topic under investigation. She is also an
active member of the FIPA agent standardisation group as the ed-
itor of the 1999 FIPA Content Language Library Specification.

Intelligent Agents
26 INFORMATIK • INFORMATIQUE 1/2000
tion has strong DAI roots and proposes that an agent is an entity
which is:
• Situated in some environment.
• Autonomous, in the sense that the system can act without
direct intervention from others (humans or other software
processes).
• Flexible, which is further broken down into three properties:
Responsive (perceives its environment and responds to
changes in a timely fashion), Pro-active (exhibits opportun-
istic, goal-directed behaviour) and Social (able to interact
with humans or other artificial agents).
This definition might be considered consistent with that of an
“intelligent agent” in the CN community. The reason for
dropping the “intelligent” is that the notion of intelligence is
difficult to pin down. Often, this becomes subdivided into
arbitrary “types of intelligence” or “levels of intelligence”. The
agent definition may also be loose enough to incorporate some
of the agents not considered “Intelligent” by the CN communi-
ty. It should also be noted that this definition considers mobility
as an optional property of an agent (or of a piece of code) rather
than something which defines a whole class of agent; helping
avoid problems such as classifying “intelligent mobile agents”.
This definition, we feel, is particularly useful since it reinforces
the view of an agent as a piece of software disposing a
fundamental set of properties. Entities displaying more or less
of these properties can then be considered more or less “agent-
like” (see Section 4).
Trends in Network Development
The main factors behind the increasing interest in agent
technology for network control can be divided into two
categories: application pull – the need for innovative solutions
to increasingly urgent network problems and technology push
– the development of new techniques which make agent
deployment a real possibility.
There are three main factors which are generating potential
need for agent based solutions are:
• Market liberalisation: The deregulation of telecommunica-
tions markets has forced major changes to the roles, business
models and operational practices of network operators and
service providers alike (in fact until the early 1990s Network
Operators and Service Providers were often one and the
same). Competition is fierce and has kick started an indus-
try-wide drive for efficiency.
• Rapidly changing technology: The number and diversity of
deployed network technologies is continually growing. This
diversification is creating a complex heterogeneous network
infrastructure and serious technological challenges in
providing uniform and coherent services. There is often not
enough time for the industry to develop guidelines before
those guidelines are already obsolete. Standard bodies (such
as the ITU, ISO, ANSI, ATM Forum and IETF) are having
to catch up with common practice rather than setting the
agenda.
• Increasing flexibility in usage requirements: With market
liberalisation and increasing customer demand comes a need
for flexible service deployment. Networks need to be
adapted to provide what customers are demanding, cope
with fluctuations in usage and handle the introduction of
new multimedia services (such as video, audio, Internet
telephony and e-commerce related communications).
These three factors together are combining to produce very
complex network architectures and requirements. Issues of
scalability, reliability, security and interactions between
services are increasingly replacing any other concerns network
operators may have had. Section 3 picks out some key areas
where agent technology may be able to play a leading role in
solving some of the most pressing network control problems.
Until recently, many agent applications have remained
nothing more than small pilot projects in the research laborato-
ry. One key reason behind this is that the necessary network
architecture for agent deployment was just not available. This
is changing. There are three main areas of technology push:
• Mobile Agents: The utility of mobile agents and mobile code
for network control has been a recurring theme since the
early 1990s. This paradigm is now beginning to gain wider
acceptance in the CN community (see works such as [Baldi
et al. 97] and [Breugst/Magedanz 98] for example). Conse-
quently, the likelihood that agent capable platforms might be
supported by future networks is increasing.
• Programmable Networks: Researchers in the relatively new
field of Active Networks [Tennehouse et al. 97] argue for
programmable networks which can receive and execute
code on time scales down to single packet arrival. Programs
can be downloaded to a router using a “backdoor” mecha-
nism or injected into the network in the headers of individual
data packets. Either way, this type of programmable network
would greatly increase the scope for the deployment of
agent based network control services into the network
infrastructure.3
• Standardisation: Ongoing standardisation efforts within
bodies such as OMG and FIPA are providing standard inter-
action mechanisms for agent based software. These efforts
to provide interoperability for agent applications are a key
factor in enabling the use of agent technology for a large
range of tasks – including network related applications (also
see Section 4).
Above all, agent technology is maturing as a software
development paradigm. Developping environments and stand-
ards is becoming available. This trend is likely to build confi-
dence in agent techniques and allow more wide-spread experi-
mentation.
Key Application Areas
Agent technology has been proposed for a large number
of network related areas (publications easily run to the 100s).4
This section picks out three areas of network control in which
agent technology may have real potential to make a difference.
2
3. Some CISCO Systems routers in fact already include Java Virtual
Machines, however their interfaces have not yet been made avail-
able to network engineers.
3

Intelligent Agents
3.1 Multi Provider Environments
Market liberalisation and increasing demands for the
allocation of services which span several networks are pushing
every network provider to evolve the way they interact with
peer operators. In order to understand what “to evolve” means,
several factors have to be taken into account.
• Heterogeneity: What were once state monopolies control-
ling everything from end user access down to the copper
wires have become several layers of competing firms –
service providers, networks providers, brokers, etc. Distinct
networks can be based on different technologies and can
deploy different network management platforms. This
implies heterogenity also of the information models used in
different networks, i.e., different Management Information
Databases (MIBs).
• Distribution of resources: Network resources can be owned
by many different “authorities”, that need to be made to
work together to support advanced services spanning several
domains (Virtual Private Networks crossing different
networks for example). This task is even more delicate for
networks which aim to provide any kind of Quality of
Service guarantees, since individual providers are unwilling
to release detailed information about the state or topology of
their internal network.
• Flexibility: Currently, many aspects of the interaction
between distinct networks, are statically fixed by contracts
(number and available capacity of links connecting one
network domain to another, prices, etc.) and many steps of
the interaction are regulated by human operators via fax, e-
mail, etc. This makes the overall inter-interoperability
process very slow (several months can pass before effective
inter-domain network configuration changes take place) and
inefficient.
• Robustness: A further major problem is that there is little or
no infrastructure to support robust information exchange or
coordination between different service and network
providers. In the best case, TMN compliant networks use
standard TMN-X interfaces which provide a rudimentary
low level interface for synchronising the settings in routers
and other network elements. A common database, the
Shared Management Knowledge (SMK), allows the
visualisation of a minimal amount of information that needs
to be shared for the interaction. Even in this case however,
humans are responsible for supervising and controlling the
interaction.
Considering these aspects, what seems more suitable for
future networks is a management solution based on static
and/or mobile software entities, collecting network state infor-
mation and which have the ability to directly invoke effective
changes to switch controllers, without the interaction of a
human operator (see [Posladt al. 99], [Corley et al. 98],
[Calisti/Faltings 99] and several works in [Hayzelden/Bigham
99]). Software agents have strong potential since they can be
distributed, intelligent, expert, heterogeneous, self-learning
and dynamic [Bigham et al. 99]. As concrete examples of
potential agent usage in the multi-provider framework, (see
Figure 1) software agents acting on behalf of every network
operator could:
• Reduce the need of human interventions and communica-
tions.
• Abstract from technical details, such as SNMP primitives or
CMIS/CMIP terms, and translating them into a more under-
standable form for human operators.
• Automate the control of switches and routers, i.e., active
routing.
• Provide automatic service negotiation with both peer opera-
tors and final end-users.
In [Calisti et al. 99] a multi-agent paradigm for the automatic
allocation of inter-domain service demands is defined. Here
one of the main challenges is to find a way of making use of
restricted information to make adequate routing decisions
when passing through domains controlled by several different
authorities (see [Calisti/Faltings 99] for more concrete results).
3.2 Resource Management
Despite predictions of bandwidth glut ([Smith 99] amongst
others), network resource management remains a very
challenging area. In the United States, backbone deployment is
a race against time and user demand, fluctuations and routing
errors can have catastrophic results5
. With the rapid rise of the
Internet as an essential business tool there are also concerns
about the potentially serious effects of prolonged periods of
poor or reduced service. This factor, above all, is driving
companies to demand 1) improvements in the overall service
quality the Internet provides and 2) the minimisation of
potentially damaging periods of poor service. These demands
need to be met with increasingly sophisticated techniques for
resource management (such as the efforts now going on under
the umbrella term of Traffic Engineering [Bhaniramka et al.
99]) both at the backbone level and at the IP network level.
These efforts correspond to controlling resource allocations in
the network to improve the use of the available infrastructure.
This is either done using reservation protocols (such as RSVP
[rfc2205]) or in an across the board fashion. Agent technology
has already been applied to several problems in this area:
• IP routing: there have been various approaches to routing
problems but perhaps amongst the most intuitively appeal-
ing are those based on the use of mobile agents to mimic
“ant like” behaviour. First proposed in [Appleby/Steward
94] and [Schoonderwoerd et al. 97] and continued by several
other research groups, this approach simulates the trail
laying behaviour of social insects such as ants in simple
mobile agents. Individual ants migrate around the network
laying and reinforcing trails on their chosen paths. Packets
4. Note also that many “network supported” agent applications are
also being developed (such as agent based information services,
web auction houses, information filters etc.) which have greater or
lesser contact with the network infrastructure. This work is not
treated here. See sources such as proceedings of the PAAM series
of conferences (http://www.practical-applications.co.uk/PAAM/)
for this type of application.
5. Such as the almost global outage due to an single erroneous
router configuration on March 25, 1997 [Labovitz et al. 98].

Intelligent Agents
(or calls) can then be routed along the strongest re-
reinforced trails which are biased to be the shortest and
encounter the least congestion (ants leave stronger signals
when arriving at a destination more quickly than their
counterparts). This approach has recently been adapted to IP
networks [Subramanian et al. 97] and developed to produce
more general protocols [Chen et al. 99].
• Bandwidth commerce: is a newly emerging model for net-
work resource management which is based on owners of
network infrastructure selling spare capacity in open mar-
kets. This trade is already happening6
and is currently car-
ried out by human operators bidding for bandwidth goods.
Agent based interactions could potentially provide much
more flexible interaction using standard agent interfaces, au-
tomatic negotiation, bargaining over bundles of goods
(which involves complex reasoning) and, not least, saving
the patience of human operators. Projects that are addressing
these possibilities include MACH7
. Market based resource
allocation has also previously been tried for off-line alloca-
tions in work such as [Gibney/Jennings 98] and [Wellman
94].
• Connection-oriented networks: work including [Hay-
zelden/Bigham 98] and [Willmott et al. 99] has shown how
agent systems could be used to control resources in back-
bone networks (based onATM or other con-
nection oriented technologies). These
methods are particularly applicable for net-
works where large amounts of state data is
generally needed to make routing deci-
sions. To fully automate the network it
would seem a logical progression to allow
agents managing IP network resources to
communicate with agent counterparts
charged with managing backbone resourc-
es.
In general terms, network resource manage-
ment can be described at many levels of gran-
ularity (from the routing of a single packet and
the routing of a flow up to the implementation
of network operator allocation policies).
Agents with properties such as those described
in Section 1.1 are more appropriate at the
higher levels of this description.
Figure 2 shows a two tier model often
applied in control problems (also adopted in
[Hayzelden/Bigham 98] and our own work).
Control is divided into two systems: an on-line
system responsible for fast time scale alloca-
tions (packet route decisions for example) and
a background system which monitors, controls
and updates the faster on-line system. The on-
line system makes most of the day to day rout-
ing allocations, however the supervisory sys-
tem would intervene if (for example): failures occur, operator
policies change, traffic congestion appears to be building or
certain types of traffic need to be specially treated. For this ap-
plication agents appear to be particularly suitable for the super-
visory style of control system since they can:
• monitor and react to the environment – hence pro-actively
deal with undesirable traffic patterns in the network,
• provide control in localised areas of the network using only
local information,
• communicate with each other to ensure that a more
coherent, global resource allocation policy is enforced.
3.3 Communications Integration
There is a clear trend towards providing the user with servic-
es rather than network access or bandwidth. Furthermore, users
are increasingly demanding that information services (such as
news, e-mail, fax, telephone etc.) are integrated seamlessly.
These demands require multiple services provided by various
network technologies to be coupled together effectively. The
types of integration required can be broadly classified into two
types: interface integration and network integration.
• Interface integration: the integration of many network
services requires complex coordination between network
infrastructure, end devices and services. Agent based
approaches have already been tested for this type of problem
(see [Abu-hakima et al. 96] for example which uses a
purpose built LAN). The integrated network service should
ideally:
6. See http://www.ratexchange.com/, http://www.band-x.com/, and
http://www.interxion.com/ for example.
7. http://liawww.epfl.ch/~ calisti/MACH/mach.html
Fig. 1: “Agentification” of the future networks: traditional management tasks and
typical human interactions are carried out by software agents.
GUI
interface
agent
negotiator
wrapper
agent
network
management system
GUI
interface
agent
negotiator
wrapper
agent
network
management system
human-agent interface
agent-interaction
interfaces
TMN-X
Gradually replaces human interaction
X
X
X
X X
X
Network A
e.g. CMIS/CMP
Network B
e.g. SNMP
MIBs
X
Agent Switch

Intelligent Agents
– Allow the addition of new services, network technologies
and end devices (such a new pager) dynamically – deliv-
ering the communications service over the newly added
medium when appropriate.
– Group together existing services to make them appear as
on “virtual service”, for example integrating voice mail,
fax and e-mail by delivering the messages arriving over all
three media in whichever of the three formats is currently
most appropriate.
The top level of this service integration is human-machine
interaction which is in turn supported by coordination in the
network infrastructure to carry out the required services.
The key advantages of agents here are in their pro-activity
and flexible interaction with the environment. Agents enable
the integration of humans and diverse hardware or software
entities by adapting their behaviour to individual preferenc-
es, characteristics of users and characteristics of the network
hardware [Fipa 98].
A concrete example of this kind of integration is the effort to
provide a Virtual Home Environment (VHE) for 3rd
generation mobile phone systems (UMTS). The aim is to
have mobile phone users presented with the same options,
services and interfaces wherever he or she is in the world
and whichever mobile phone provider is currently providing
these services. Both static [Lloys/Pearmain 99] and mobile
agent (the EU ACTS “On the Move”8
project for example)
approaches have been proposed for this problem.
• Network integration: as the number of deployed network
technologies grows, providing homogeneous services
requires abstraction from technological details and stand-
ardised models for communication. Additionally, different
functional parts of the infrastructure may be owned by
different companies with, for example, service providers
leasing bandwidth from network operators.
Applying agents to integrate heterogeneous networks and
network technologies has been proposed both within the CN
community (in the TINA framework9
) and within the DAI
community (with the FIPA agent network management
model, Section 7 of the 1997 FIPA Specification [Fipa 97]).
In the TINA architecture, software entities interact with both
humans and physical network devices, communicating over
a distributed execution environment. The FIPA architecture
further encompasses the notion of different authorities
owning different levels and parts of the network and looks to
address the question of establishing end-to-end services
over several (separately owned) networks (hence similar to
the issues discussed in Section 3.1).
This trend towards integration in all directions looks set to
continue and is perhaps one of the most challenging problems
networks of the future will have to face. In this area the concept
of agent middleware which bridges the technological and
architectural gaps in current systems seems to have great
potential (see [Poslad et al. 99]). Agents provide a means of:
• Abstracting from the technological idiosyncrasies of differ-
ent network technologies to improve their interoperation.
• Enabling richer and more flexible interaction between both
user and system (user network service access) and system
and system (automatically exchanging tasks between
different agents to customise service delivery).
The Agent Future?
The three key application areas discussed in Section 3
cover a large part of the communications network infra-
structure, however this is not intended to advocate the use of
“agents everywhere”. The type of software agent which fits the
definitions given in Section 1.1 would arguably be inappropri-
ate for tasks which:
• Required vary fast repetitive processing: the utility of using
agents is generally in providing flexible execution behaviour
to function correctly in a dynamic environment. By its very
nature this type of processing is likely to be less efficient for
highly constrained, repetitive tasks (such as packet
forwarding).
• Required rapid and precise information exchanges: particu-
larly in cases where agents communicate using standard
agent communication languages such as KQML [Finin et al.
93] and FIPA ACL [Fipa 97], the flexibility in agent
communication may be problematic. For many network
tasks, highly constrained, concise protocols are the best way
of exchanging information.10
• Need to execute on very low specification devices: agents
may well be pieces of software of substantial complexity
and not be able to run everywhere. This obstacle is gradually
being removed by smaller footprint agent platforms and
more performant network devices.
8. http://www.sics.se/~ onthemove/
9. http://www.tinac.com/
10. This is not to say agents cannot also employ these protocols, how-
ever DAI purists might argue these do not completely fill the role
of agent communication.
4
Fig. 2: The routers in the network each have an on-line
allocation mechanism. Agents communicate with each other
to resolve longer term allocation problems, occasionally
intervening in the on-line system’s operation.
Agent Architecture
Running Network
NetworkNodewith
on-line routing
mechanism
Agent Controller with
backgroundinfluence
over a set of network
nodes

Intelligent Agents
Passing down the network stack and to operations which
need to be carried out at faster and faster time scales, one would
expect control software to have less and less of the features
listed in Section 1.1. However, this change is likely to be a
continuum rather than a sharp break and deciding where the
dividing line between “agent” and “non-agent” will perhaps
become somewhat academic (Figure 3).
In summary, entities near the bottom of the stack (such as an
SMNP agent or deployed service code) may be mobile and/or
have very limited tasks where as entities higher in the stack
(such as top level managers or user interface agents) may begin
to have properties which a DAI researcher might find agent-
like. The two tier resource management model given in Section
3.2 illustrates this idea: agents are applied as layers of super-
visory systems controlling layers of increasingly constrained
and optimised on-line systems. This model is analogous to
what already goes on in networks today and is characterised by
[Musliner et. al. 95] as intelligent reasoning about real-time
processes.
Aside from determining where in the network agents should
be deployed, there are also wider considerations which need to
be addressed before agent technology can realise its full
potential in communication networks. The future deployment
of agent technology rests critically on building increased
cooperation between the traditionally separate DAI and CN
communities. Apart from the terminology problems already
mentioned, the division has created other obstacles to
development:
• Continuing (to some extent justified) scepticism on the part
of communications network engineers as to the utility and
suitability (in terms of security, robustness, speed of
operation etc.) of agent technology. This has resulted in a
lack of tested practical solutions and many approaches
which have never made it beyond the test bed stage.
• The biggest stumbling block for DAI researchers has
perhaps been the technological complexity of the networks
being studied. It would be fair to say that several of the
promising methods developed by DAI researchers in the
past have met with little success due to failings in the
starting assumptions about the network domain.
• Agent solutions which have been proposed by the CN
community have remained very simple and not leveraged
some of the more powerful techniques developed by the DAI
community.
There are indications that this collaboration is increasing and
that the interests of the two communities are growing together.
The papers presented at the Smartnet11
and DSOM12
work-
shops this year, for example, include a significant number of
agent related papers. Furthermore, the continuing interest in
agent technology within the OMG and FIPA standards bodies
for example (both of whom list many member companies heav-
ily involved in communication networks including: British
Telecom, France Telecom, Nortel, Motorola and many others)
is encouraging. The European Union AgentLink project13
is
also contributing to this collaboration with a special interest
group dedicated to the application of agent technology to tele-
communications networks.
Conclusions
Having covered three areas which might greatly benefit
from the application of agent technology and discussed some
of the provisos in its application, we can conclude by advanc-
ing three main reasons for believing that future network
developments may include the deployment of agents:
1. Need for innovation: Increasing competition, technological
complexity and usage requirements are all contributing to
increased strain on network infrastructure. This push is
making innovative solutions (and potentially agent
solutions) to network problems vital for ensuring continued
good service.
2. Technological feasibility: Agent technology is maturing as a
software paradigm. Alongside the increasing availability of
development environments it is increasingly likely that
deployed network equipment will in the future be able to
support the computational needs of agents.
3. Increase industry openness: As the continuing collabora-
tions within FIPA, OMG and many European projects show,
11. http://www.cs.ait.ac.th/~ca/smartnet99/
12. http://www.tik.ee.ethz.ch/dsom99/ 13. http://www.agentlink.org/
5
Fig. 3: In an “agentified” model of the network software entities
operate at different levels. Physical Agents (PA) might control
specific network elements (as simple input-output sensors for
example). Resource Agents (RA) might invoke changes in the
switches and routers using information coming from both the
higher and lower levels in the network. Mediator Agents (MA)
might be more sophisticated entities needing to be able and
inter-operate with other entities by using a common agent
language. Finally, Interface Agents (IA) could translate from
agent languages to more human understandable information.
The lower down through the layers an entity resides, the less
sophisticated it is and the less developed its “agent properties”
might be considered to be - the decreasing sophistication is
illustrated by an increasingly dashed line.
IA
IA
MA
MA
RA
RA
PA
PA
PA
PA
interfaces
interfaces
X
X
applikation
presentation
session
transport
network
link
physical
Osi reference model

Intelligent Agents
the communications network industry is increasingly open
to experimentation with agent based solutions.
Together, these reasons suggest that there could be a slow
agentification at least of the upper layers of the network infra-
structure – little by little – agents may begin to appear in our
networks. The technologically dynamic communications
industry is however known for its frequent changes of tack so
only time will tell.
Due to the limited space available this article can only give a
brief overview of the subject area. We hope to have included
enough pointers to literature (in particular see the survey
articles referenced in Section 1) to serve as a useful starting
point for further reading.
Acknowledgements
The authors would like to extend their thanks to the other partners
in the SPP-ICC IMMuNe project (which partly funds this work).
Funding for IMMuNe from the Swiss National Science Foundation14
is also gratefully acknowledged.
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