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.

A Multi-Agent Learning Language (MALL) is defined as being necessary for agents in environments where they encounter crucial situations in which they have to learn about the environment, other parties moves and strategies, and then construct an optimal plan. The language is based on two major factors, the level of certainty in fully monitoring (surveying) the agents and the environment, and optimal plan construction, in an autonomous way. Most of the work related to software agents is based on the assumption that other agents are trustworthy. In the growing Internet environment this may not be true. The proposed new learning language allows agents to learn about the environment and the strategies of their opponents while devising their own plans. The language is being tested in our project of software agents for Electronic Commerce that operates in various security zones. The language is flexible and adaptable to a variety of agents applications.