A framework for constructing multi-agent applications and training intelligent agents
As agent-oriented paradigm is reaching a significant level of acceptance by software developers, there is a lack of integrated high level abstraction tools for the design and development of agent-based applications. In an effort to mitigate this deficiency, we introduce Agent Academy, an integrated development framework, implemented itself as a multi-agent system, that supports, in a single tool, the design of agent behaviours and reusable agent types, the definition of ontologies, and the instantiation of single agents or multi-agent communities. In addition to these characteristics, our framework goes deeper into agents, by implementing a mechanism for embedding rule-based reasoning into them. We call this procedure ‘agent training’ and it is realized by the application of AI techniques for knowledge discovery on application-specific data, which may be available to the agent developer. In this respect, Agent Academy provides an easy-to-use facility that encourages the substitution of existing, traditionally developed applications by new ones, which follow the agent-orientation paradigm.
P. A. Mitkas, D. Kehagias, A. L. Symeonidis, I. N. Athanasiadis, A framework for constructing multi-agent applications and training intelligent agents, Lecture Notes in Computer Science (Agent Oriented Software Engineering IV), vol. 2935, pg. 96-109, 2004, Springer, doi:10.1007/978-3-540-24620-6_7.
You might also enjoy (View all publications)
- Mixing process-based and data-driven approaches in yield prediction
- Combining telecom data with heterogeneous data sources for traffic and emission assessments - an agent-based approach
- A weakly supervised framework for high-resolution crop yield forecasts