Intelligent Agents in Telecommunications

Intelligent Agents in Telecommunications.
Nwagu, Chikezie Kenneth; Omankwu, Obinnaya Chinecherem; and Inyiama, Hycient
1
Computer Science Department, Nnamdi Azikiwe University, Awka Anambra State, Nigeria,
Nwaguchikeziekenneth@hotmail.com
2
Computer Science Department, Michael Okpara University of Agriculture, Umudike
Umuahia, Abia State, Nigeria
saintbeloved@yahoo.com
3
Electronics & Computer Engineering Department, Nnamdi Azikiwe University, Awka
Anambra State, Nigeria.
ABSTRACT
The underlying fabric for communication among intelligent
agents will in many cases be provided by telecommunication
networks. But telecommunication networks have been seen as
a natural domain for the investigation and application of
intelligent agents’ technology as it emerged from the area of
Distributed Artificial Intelligence (DAI). Telecommunication
network administrations are vast organizations dedicated to
operating and managing networks with broad functional
segmentations: telephone network outside plant, switching and
transmission plants, public network, all supporting different
layers of specialized customer or service networks. These
networks are organized into multiple physical and logical
layers built with large quantities of repeated network elements
and sub network structures. All these elements need to be
configured, monitored, and controlled. In the future, this will
preferably be done by automated operation support systems
and without substantial human intervention.
Keywords
Distributed Artificial Intelligence (DAI), Telecommunication
network administrations.
INTRODUCTION
The telecommunication networks have been seen as a natural
domain for the investigation and application of intelligent
agents’ technology as it emerged from the area of Distributed
Artificial Intelligence (DAI). Telecommunication network
administrations are vast organizations dedicated to operating
and managing networks with broad functional segmentations:
telephone network outside plant, switching and transmission
plants, public network, all supporting different layers of
specialized customer or service networks. These networks are
organized into multiple physical and logical layers built with
large quantities of repeated network elements and sub network
structures. All these elements need to be configured,
monitored, and controlled. In the future, this will preferably be
done by automated operation support systems and without
substantial human intervention.
Although many recent efforts in applying DAI techniques
have come from the telecommunications industry, the legacy
information infrastructure that services interoperations in this
industry has so far dampened immediate attempts at
introducing DAI-based systems in the field. Consequently, the
experimental DAI-based systems that have been developed
for telecommunications have been primarily aimed at future
networks and laboratory environments.
This paper presents a brief survey of recent efforts in
applying DAI to telecommunications including some of our
specific experiences with such applications. Applications and
domain descriptions thus take precedence over focused
analytical study of the corresponding agent systems and their
theoretical justifications.
Our communication engineering and automated 'coarse-
grained' co-operative agent perspective leads us to omit
treatment of human-computer interaction themes, 'information
agents,' etc. Thus, we do not consider here applications based
on mobile agents and scripting languages such as offered by
Java and TeleScript, which lack explicit support for co-
operative behavior, even though these types of agents have
received a lot of attention in the telecommunications domain
over the last couple of years. This is not to say that this
technology is not promising. In fact, the commercial
application of mobile agents and scripting languages may be
closer than that of intelligent, co-operative agents.
Overview of Distributed Artificial Intelligence (DAI) and
Telecommunications
After providing the basic rationale for DAI and
telecommunications, this section briefly surveys the field with
a small classification that also serves to ground the
bibliography.
Rationale for DAI in Telecommunications
Inherent distribution and interconnectedness of
telecommunication networks and their constituent components
provide the basic rationale for DAI approaches to the solution
International Journal of Computer Science and Information Security (IJCSIS),
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331 https://sites.google.com/site/ijcsis/
ISSN 1947-5500

of telecommunication problems. DAI approaches address a
number of dimensions of this distribution:
1. Between physical network elements and logical network
management layers, such as facility agents and customer
agents in TEAM-CPS, e.g., (Appleby, 1994; Weihmayer,
1992).
2. Between information, knowledge, and capabilities
belonging to different entities and roles in a
telecommunication system and its environment, such as
the service profiles of different users in the negotiating
agents approach, e.g., (Griffeth, 1993b).
Distribution is not the only criterion for determining the
relevance and appropriateness of application of DAI
techniques. DAI can be rationalized in terms of performance,
partitioning global information (privacy domains), and
requirements for intelligent co-ordination and negotiation
between the different agents (users, subscribers, service
providers, network operators, etc.) of future
telecommunication software infrastructures.
Broad Survey of Applications
DAI techniques have been applied to a wide range of different
telecommunication problems. This is illustrated in Table 11.1.
First, we recognize the following layering of
telecommunication systems: the transmission and switching
layer, the network control layer, and the service layer; second,
we distinguish between the network development and
maintenance tasks of network design, network manage-ment,
and service management; and third, we single out process
support, i.e., systems that support and automate (typical
telecommunications) business processes.
We are aware of one application in the transmission and
switching layer. Nishibe et al. have presented an approach to
dynamically allocating channe1s in an ATM (asynchronous
transfer mode) network.
We are aware of one application in the transmission and
switching layer. Nishibe et al. have presented an approach to
dynamically allocating channe1s in an ATM (asynchronous
transfer mode) network.
Two applications focus on the network control layer: the
work by Fleteher and Deen (Fleteher, 1994) on dynamic
routing and congestion control; and some of the work by
Weihmayer et al. (Weihmayer, 1993), on mediation between
control wielded by public network provisioning systems and
integrated private network management systems.
One application focuses on the service layer. This
approach uses DAI techniques to mediate between preferences
users may have for the behavior of a telecommunications
system and as it is typically implemented by their subscribing
to particular service features (Griffeth, 1993); This work
builds on an agent-based service execution environment on
top of a network. This approach has since been adopted by
various others (Busuoic, 1996; Rizzo, 1995; Zibman, 1996). A
related approach uses mobile agent technology within the
constraints of the Intelligent Network architecture to offer
services in a fully deregulated environment (Magedanz, 1996).
The work by Pujolle proposes to use DAI techniques to
integrate standardized architectural concepts for the areas of
service provisioning (in the service layer), service
management, and network management.
Service management is furthermore addressed by
Weihmayer et al. (Weihmayer et al, 1992) and Griffiths and
Whitney in their applications of DAI to distributed service
provisioning among service maintenance agents and customer
agents.
Network management is the most frequently addressed
application area. Three of the more advanced applications in
this group are described in more detail in Section 11.3. In
network management, a distinction can be made between the
traditional network management functions: configuration
management, fault management, accounting management,
performance management, and security management. Not all
of these functions have been addressed by DAI approaches:
accounting and security management are, as yet, uncharted
grounds, although Reilly has studied in particular the security
aspects involved in extending TMN-based network
management systems with mobile agent technology.
In several cases, one system combines more than one
functionality, e.g., fault and performance management, but
most approaches focus on fault management. We mention:
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Work on the Multistage Negotiation protocol developed with
the task of restoring transmission paths in telecommunication
networks in mind (Conry, 1991);
Two applications are mentioned in the literature for
network design: one by Lirov and Melamed (1991) where
loosely coupled expert systems perform sub-sequent tasks in
finding an optimal network configuration; and one by van
Liempd et al. (1990) where blackboard-based nodes are used
to configure sections of network connections spanning
multiple administrative domains.
The last application referenced in Table 11.1 addresses a
process support task which is particular to
telecommunications, requiring co-ordination among many
operations support and database systems.
Several activities studying the application of DAI
techniques to telecommunications have abstracted from
particular network architectures workflow management for
support of provisioning of digital telecommunications
services, but others address a particular type of network.
TCPIIP-based LANs have been used in a number of
applications (So, 1992; Sugawara, 1992), probably because
this type of network is readily accessible for experimentation
in many research organizations. One application looked at
network configuration of a leased-line corporate network
(Liempd, 1990). The work by Weihmayer et al. addressed the
interplay between a virtual corporate networks implemented
over a voice/data PBX network with public Tl links
(Weihmayer, 1992). The MAlTE system was targeted at a
network of 800 interconnected PBXs (Garijo, 1992). In
(Nishibe et al., 1993), Nishibe et al. describe an application
for the ATM network concept.
Present-day public telephony networks have not been the
subject of detailed study according to our survey.
DAI Techniques.
It may be too early, given how few implementation efforts we
have found, to begin a classification according to DAI
techniques used. We found that a few well-established DAI
techniques are mostly referred to in current applications to
telecommunications, for example: blackboard systems in
(Adler, 1989; Garijo, 1992; Liempd, 1990); the Contract Net
Protocol in (Biron, 1992; So, 1992).. Most other references
use specialized representations or algorithms for such aspects
as agent architecture and organization, negotiation and co-
ordination, planning, etc.
Overview of Four Applications
The significance of the four DAI applications presented in this
section lies in the range of DAI techniques used, the
underlying domain analysis, and the extent of experimentation
work.
'Distributed Big Brother': Campus Network Management
'Big Brother' is a centralized LAN manager operating at the
University of Michigan's Computer Aided Engineering
Network (CAEN). 'Distributed Big Brother' (DBB) is a
research test bed built at CAEN to investigate higher
performance and more robust LAN management techniques
using a combination of a number of DAI techniques.
The specific domain under consideration is management
of campus-level LAN inter connect systems, i.e., multiple
LANs connected with bridges, routers, gate-ways, etc.,
(spanning less than 10 miles) without intervening WAN. The
target functionality is system support, specifically for fault
management (So, 1992)
The operational requirements for DBB include
maintaining a centralized view of network fault management,
improving the robustness of network management processing,
and increasing its performance and parallelism. Individual
LAN segments and local subnets are loosely coupled in this
domain but interdependent inasmuch as they share
components, e.g., multiport bridges, and as decisions
regarding these components have impact across subnet
boundaries. In DBB, those are the entities across which
distribution takes place.
The rationale for a DAI approach is to manage a distributed
set of homogeneous LAN managers as a dynarnically self-
reconfigurable organization driven by fault conditions in the
campus LAN network. This organization is hierarchical
because centralized control remains embodied in a root
management agent that maintains a global network view but
operates under a delegation of authority scheme to manage
distributed manager agents. The intelligence of co-operative
agents is thus geared toward maintaining this organization,
through organization structuring and hierarchical control;
performing load management, e.g., which agents manage what
entities in the network; as well as contracting and voting to
distribute roles.
DBB agents are fairly large and complex entities loosely
coupled in their activities. No single DBB agent, regardless of
organizational role or position in the hierarchy, can be a single
point of failure. DBB uses the Contract Net Protocol to
announce, bid on, and award roles and tasks. Hierarchical
control is applied within the context of election procedures,
with simple rules to designate agents that officiate election to
network management roles.
Agents are structured as interacting communication,
contracting, and task processes. They use control problem
solving and domain problem solving message types. DBB
agents are homogeneous and are implemented as UNIX
processes in their host workstation. An internal rule base
specializes agent behavior based on dynamically assigned
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roles. Inter-agent communication occurs 'in-band' using UDP
over the TCPIIP LANs managed by DBB.
Customer Network Control and TEAM-CPS
TEAM-CPS (Testbed Environment for Autonomous Multi-
agent Co-operative Problem Solving; Tan, 1992; Weihmayer,
1992) evolved from a number of communication domains,
each requiring distributed problem solving among geo-
graphically and functionally distributed problem solvers
performing inter-dependent planning tasks. An industry
perspective on this effort is provided in (Mantelman, 1990).
Customer networks are integrated voice/data networks in
which switching and transmission facilities can be owned by
the customer and/or leased from the public network. A
distributed co-operative agent-based architecture for customer
network control was developed. Facility provisioning in the
public network is assigned to a computer system with the
ability to satisfy real-time demand for circuit addition and
deletion requests from multiple customer network systems.
This is the physical network layer, in contrast to the logical
customer network layer.
The DAI approach to this problem models the joint problem
solving and co-ordination that the respective network
managers go through in resource constrained situations where
compromise solutions need to be found to partially or fully
restore failed circuits. The problem solvers are by necessity
distinct and heterogeneous; the customer manages his logical
network layer, which is functionally separated and involves
private data and proprietary operations on services, traffic,
and usage; the same applies to the public network, which
delivers a complex service using a distinct and private
infrastructure in a competitive environment. The local
problem solving required is in both cases complex and
knowledge intensive, and integrates lots of information, e.g.,
fault management, trunk network design, and provisioning in
the public network; and traffic, service, session, and
configuration control in the customer network.
TEAM-CPS evolved as a framework for building co-
operative agents that are heterogeneous at the domain
problem solving level but for which common agent control
abstractions can be applied to co-ordinate their problem
solving behavior. Customer and public network agents share
agent control and problem solving mechanisms but can be
endowed with different domain models for planning and
problem solving as weH as different control policies,
represented as mIes in agent programs. The TEAM-CPS
framework integrates Agent-Oriented Programming (AOP)
and c1assical AI planning as the 'head' and 'body,'
respectively, of problem solving agents.
A more flexible and general agent control structure was
needed to model plan interactions using higher level
knowledge and to deal with pragmatic issues in managing
compromise and query dialogues among the agents. Agent-
Oriented Programming (Shoham, 1993) is a framework that
integrates agent control and communication. In AOP, agents
have persistent mental states that use techniques of modal and
temporal logic to represent agent beliefs and commitments to
other agents. Speech-act models of communication are used to
support simple communication to inform, request, and commit
about actions.
TEAM-CPS integrates this form of agent control with
planning-based problem solving by providing common
representation of world states in planning and belief in agent
mental states. Agent programs are constructed with simple
heuristics to generate proposals and counterproposals. Co-
ordination in systems of up to three agents was studied.
LODES: Distributed TCPIIP Fault Management
LODES (Large-intemetwork Observation and Diagnostic
Expert System) are an expert system for detecting and
diagnosing problems in a segment of a local area network
(Sugawara, 1992). Different LODES system copies, or
instances, can monitor and manage different network
segments. LODES is designed in such a way that LODES
instances co-operate with each other as agents of a multi-
agent system. Co-operation is necessary when it is not c1ear
in which segment the cause of a problem is located, when a
problem occurs in a part of the network monitored by more
than one LODES agent, or when one agent needs additional
information from another agent to perform its own tasks.
LODES were developed for TCPIIP local area networks
(LANs) that consist of several constituent networks connected
through routers. Each constituent network has its own
LODES diagnostic system. LODES address connection
establishment problems due to unintentional disconnections,
slow transmission, and net-work congestion which arise in
such networks.
An expert system approach was taken to overcome these
difficulties in the diagnostic task. Furthermore, a distributed
approach was chosen over a centralized approach to reflect
natural physical and functional distribution of net-works, to
localize private information, such as passwords, within the
boundaries of separate administrative domains, and to increase
performance.
The LODES system was primarily developed as a research
testbed; however, the system has been tried on real equipment
and laboratory-configured networks with some success.
Feature Interaction and the Negotiating Agents
Approach
The negotiating agents approach was developed to detect and
resolve certain kinds of feature interactions that occur in
telecommunications systems (Griffeth, 1993). Features in a
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telecommunications system provide packages of added
functionality to basic communication services (e.g., call-
forwarding and caller-ID). The operation of one feature may
influence that of another feature. Sometimes such influences,
called feature interactions, are intended. However, at other
times, these interactions can be unexpected and undesired and
need to be detected and resolved.
Features are a way for subscribers to tailor their use of
telecommunications services to their specific needs and
intentions. However, different parties to a call may have
different needs and intentions, which lead to conflicts between
the intentions and, analogously, to interactions between the
corresponding features.
The negotiating agents approach recognizes that a
significant sub set of feature interactions are the result of
conflicting intentions of different subscribers, who are not
necessarily party to the same call. These conflicts may arise
over which medium and protocol to use for a call, over the use
of shared resources such as terminals and bridges, over what
information can be transmitted as part of a call, etc.
The negotiating agents approach uses an agent for each
entity that may have an interest in how calls are set up and
conducted in a telecommunications system (users, network
providers, information providers, etc.). An agent is given
information about the intentions of its owning entity and
negotiates with other agents to detect and resolve conflicts.
The negotiation mechanism was specifically designed for this
application to search for winlwin solutions and to deal with
agents that are not prepared to disclose too much about their
preferences. A DAI approach was chosen to separate user
preferences and to build on DAI expertise in the area of
automated negotiation processes.
The negotiating agents approach has been implemented
and tested on top of a multimedia desktop conferencing
research prototype.
DAI and Telecommunications: Where Do We Stand?
Although quite a bit of industrial activity in applying DAI
technology to real-world domains has been in
telecommunications (Bond, 1992; Durfee, 1991; Jennings,
1994), there are currently no fielded DAI systems in public or
private telecommunications. We discuss the issues that have
hindered the emergence of fielded DAI systems in
telecommunications so far and paint a direction in which we
expect real-world applications to come about in the next few
years.
Problems with DAI Approaches
There are several reasons why DAI systems are slow to be
fielded in mainstream telecommunication systems in spite of
the clear rationale and need for their introduction.
One reason is the absence of adequate infrastructure for
autonomous agent-based control in present-day
communication systems. Another is that the modemization of
public networks is a relatively slow process because backward
compatibility must be maintained to meet high reliability and
availability requirements. DAI adds distribution to the
complexity of AI and may thus still be seen as too high a risk.
The technology is still emerging and is only beginning to
provide a solid foundation, principled practices, and
engineering methodologies required to produce mainstream
applications. We note that the basic approach in all four
examples in Section 11.3 was to consider a problem for which
a co-operative agent solution appears to be the best approach
and where there is no 'legacy' system to integrate. Such an
approach is suitable for proof-of-concept experimentation and
allows a co-operative solution to be built from the ground up.
However, it is not conducive to fielded systems. We believe
that DAI-based systems will appear along a different path.
Actually, the penetration of expert systems may offer a good
predictor for the emergence of DAI-based applications. Good
opportunities for the success of DAI in telecommunications
will be in network management and operations support for
public and private networks. Expert systems are slowly
penetrating those areas. Our thesis is that they will provide the
intelligent systems infrastructure and potentially the agents of
future co-operative systems. Of course, many different AI and
reasoning paradigms, not just rule-based reasoning, will
provide the intelligence substrate for those mainstream expert
systems. In this perspective, we consider the emergence of
communication and knowledge sharing between these
systems as requiring a DAI technology base that goes beyond
basic co-ordination among rule-based inference engines and
their knowledge bases.
The Road to Fielded Applications: An Example
Here is an example that illustrates how we believe that fielded
DAI applications will emerge in telecommunications. A
recent survey (Goyal, 1994) provides information on expert
system deployment in various telecommunication domains.
One such system, SSCFI (Special Service Circuit Fault
Isolation; Goyal, 1994) is an example of advanced
deployment of geographically distributed local intelligence.
It is an expert system that is currently being deployed in all
the GTE telephone companies in the United States. It
functions as an expert test technician that reads and interprets
trouble reports on special service circuits; decides to conduct
tests and interpret the results of those tests, using in the
process a number of remote test and database systems; and,
formally, routes the report to the appropriate repair group with
the results of its analysis. The first important feature of SSCFI
for this discussion is its autonomy. Besides a minimal
administrative interface, SSCFI processes operate in the
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background and have full control over the trouble report
queues to which they are initially assigned. SSCFIs are, in a
sense, our model of practical, narrow, but intelligent
autonomous systems performing their tasks without direct
human operational control. These tasks are not to present
information to people, but to act within the context of a
workflow system where field repair crews are at the receiving
end.
The current evolution of SSCFI is pointing toward some
form of homogeneous multi-agent task allocation system
involving SSCFIs throughout the GTE regions. SSCFIs need
fast local access to all the OSSs, database resources, test
systems, and test points involved in testing circuits. To
maximize throughput, these resources are locally available on
their LANs. SSCFIs can do simple load balancing by
spawning child processes to jointly handle test loads. The
spawning is constrained by the availability of computing
resources. DAI begins to come into the picture as load sharing
at a national level between all SSCFIs is being considered to
exploit time zone differences and sharing of computing
resources across regions. Some ideas under discussion include
using contracting schemes to have a local SSCFI request other
SSCFIs to accept responsibility for testing circuits along with
remote control of the resources required to perform those tests
and to negotiate the conditions under which this is done.
Conclusion
The notion of intelligent agents is certainly quite pervasive
today. A special issue of Communications 0/ the ACM (ACM,
1994) shows how intelligent agents are being promoted as the
basic building blocks of information systems. If agents are
coming, co-operative agents cannot be far.
In telecommunications, requirements for intelligent agents
are even more immediate especially since te1ecommunication
networks are canonical distributed systems.
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