Although there are many projects focusing on multiagent systems, there are only a few focusing on systematic design of large scale multiagent system. In this paper we formalize the knowledge representation and sharing of agents, using symbol structures, define agencies as organizations (i. e. , a coalition of agents), propose a formalism to represent organizational Intelligence, devise a basic configuration for generalized agents (AG), and use them in a large scale multiagent system design. Private knowledge of an AG agent is represented by a symbol structure (SS) and AG agents can share their knowledge using combination, specialization and generalization methods that operate on the SS. Opposite to the other works, organizational knowledge, is defined as a property of at least a pair of AG agents.

This paper discusses a framework for automating fault management using distributed software agents. The management function is distributed among multiple agents that can carry out advanced reasoning activities on the network domain. Network domain modeling using Bayesian network is introduced. The agent detects, correlates and selectively seeks to derive a clear explanation of the alarms generated in its domain. Depending on the network's degree of automation, the agent can even carry out local recovery actions. The ideas of the paper are implemented in a software for inference in Bayesian network. We identify the potentialities of learning in the agent model, and present the class of problems to be addressed.

A major topic in the field of network and telecommunications is doing business on the World Wide Web (WWW), which is called Electronic Commerce (EC). Another major topic is blending Artificial Intelligence (AL) techniques with the WWW. In the Ex-W-Pert Project we have proposed an agent model for EC components that blends the traditional expert systems' reasoning engine with a multi-layer knowledge base, communication and documentation engines. In this project, EC is viewed as a society of software agents, such as customer, search, catalog, manufacturer, dealer, delivery and banker agents, interacting and negotiating with each other. Each agent has a knowledge-base and a reasoning engine, a communication engine and a documentation engine. The knowledge-base is organized in three layers: skill layer, rule layer and knowledge layer (S-R-K layers). In this project, for each EC agent, we identify the class of problems to be solved and build the knowledge base gradually for each layer. We believe that using this multi-layer knowledge base system will speed up the reasoning and ultimately reduce the operation costs.