  Agent Academy (AA) aims to develop a multi-agent society that can train new
agents for specific or general tasks, while constantly retraining existing
agents in a recursive mode. The system is based on collecting information both
from the environment and the behaviors of the acting agents and their related
successes/failures to generate a body of data, stored in the Agent Use
Repository, which is mined by the Data Miner module, in order to generate
useful knowledge about the application domain. Knowledge extracted by the Data
Miner is used by the Agent Training Module as to train new agents or to enhance
the behavior of agents already running. In this paper the Agent Academy
framework is introduced, and its overall architecture and functionality are
presented. Training issues as well as agent ontologies are discussed. Finally,
a scenario, which aims to provide environmental alerts to both individuals and
public authorities, is described an AA-based use case.
