Overview

Background

The introduction of the agent paradigm and the agent-oriented software engineering (AOSE) stands the traditional evolution of the informatics technologies. If in the past the new abstractions came from programming languages and following they were included in the software engineering field. Today, instead, the evolution is very quickly and the engineer often works with technologies that not support the abstraction used in the design of the systems. For this reason the research on methodologies becomes the basic point in the scientific activity. This research now focuses on the agent paradigm, whose expressivity and level of abstraction allow to deal with complex problem (component distribution, knowledge representation and reasoning, communication and cooperation among autonomous and heterogeneous entities) in efficient way. Then there is a gap between the AOSE approach and the available technologies.

The gap

The proposed AOSE methodologies have mostly followed a top-down approach, where the agent paradigm and the metaphor of the human organisation have been used to analyse, model and design a system. On the other side, multi-agent languages and tools have been evolved out of necessity from existing programming languages (mainly object-oriented languages) and development environments. This has led to a bottom-up evolution of such languages, where the agent paradigm has been stretched and used to extend pre-exiting paradigms (e.g., the object paradigm), while the creation process of new industrial languages is slowed down by the lack of standard. The gap between these approaches can leads to dangerous inconsistencies between the design and the actual implementation of the system. These are the consequences of the use of concepts and abstractions in the analysis and design stages which are different from those used to deploy and implement the system. On the one side the agent-based abstractions available in the design phase suggest high level of expressivity, on the other side the development tools, that are still in the stage of academic prototypes, don't support these abstractions.

Challenges of MEnSA

This suggests two important challenges that represent the principal objective of the project:

identification of the effective abstractions to model complex systems as multi-agent systems
integration of these abstractions in methodologies that support the whole software life cycle and fill the conceptual gap between agent-oriented methodologies and the infrastructures used to implement agent-based systems.

MEnSA structure

Starting from our experience in developing AO methodologies, infrastructures and development tools

we will draw from the many contributions in the AOSE literature to synthesize a new methodological framework that fruitfully integrates the most effective AO abstractions, concepts, processes and practices
and correspondingly build up the infrastructures and development tools that actually support and promote such a framework.