**8. Conclusion**

Current networks are very complex and demand ever-increasing levels of quality, making their management a very important aspect to take into account. The traditional model of network administration has certain deficiencies that we have tried to overcome by using a model of intelligent integrated management. To improve the techniques of expert management in a communications network, we propose the possibility of integrating and normalising the expert rules of management within the actual definition of the managed objects. Intelligent managed objects characteristics are autonomy, reactivity, pro-activeness, mobility and learning.

In this chapter we showed possibilities to apply and integrated the artificial intelligence techniques in network management and supervision, using OSI. We showed possibilities to apply and integrated the artificial intelligence techniques in network management and supervision, using ISO network management standard.

Unfortunately, the knowledge management is defined in using different intelligent techniques. This results in knowledge specifications which are often ambiguous, increasing the possibility of different implementations not being interoperable. To achieve consistent, clear, concise, and unambiguous specifications, a formal methodology has to be utilized. This paper introduces a framework for the inclusion of formal knowledge descriptions into GDMO specifications. An object-oriented logic programming language is presented, which can be used in conjunction with the framework to specify the management knowledge of managed objects.

We have supplied an original contribution to include expert rules in the specifications of the network elements; for this purpose we have proposed a new standard called Extension of GDMO standard or simply GDMO+. Through the integration of the knowledge within the new extension of the GDMO standard, we can simultaneously define the management information and knowledge. Thus, the management platform is more easily integrated and allows a better adaptation for the network management. Moreover we have built a prototype and experiments have been carried out in order to test the efficiency of our proposal. This demonstrated that GDMO+ is capable of specifying the knowledge of a reasonably sized information model. A large amount of the management knowledge could be described in a surprisingly short and easy to understand manner.

It is suggested that future work should aim to further development of this prototype system by adding more modules based on the framework provided by the system so that more indepth knowledge and specialized subjects may be captured; in particular the following are of great interest: Development of a design module, possibly a large system, for identifying specific areas like accounting management, configuration management, performance management and security management. Moreover use of external programs and graphics interface to enhance the functions of the system will be desirable. Finally study the possibility of using another method of knowledge representation and reasoning different to the rules: Semantic nets, neuronal nets, frameworks, etc.
