Introduction

The general framework of the project is the study of the agent paradigm and its intrinsic mechanisms in order to model complex systems. A complex system can be defined as a system in which any algorithm could describe the behaviour, and where mathematical models do not provide efficient solutions to understand and then to predict underlying phenomena. One of the privileged applications of such a framework is natural phenomena modelling. The issue is not to model some existing systems which are naturally distributed, but rather to model complex systems with artificial systems. This point of view can be, at a first glance, associated to C. Langton's works on artificial life  [5].

In our project, a simulation application to help in predicting volcanic eruptions has been investigated. To try to understand complex behaviours, a computational model with communicating agents is then considered, in which emergent phenomena arise through interactions between local entities and their environment. Such a multi-agent system is described in a layered architecture, called GEAMAS (acronym for GEneric Architecture for MultiAgent Simulations). GEAMAS is seen as a multi-agent software platform, intended to develop simulation applications. Envisaging a layered architecture allows a better understanding of how the emergence of a global behaviour occurs, and why the multi-agent approach works in this context. This architecture is based on three abstraction levels. Each successive level represents a higher level of abstraction, and describes a complex degree of knowledge.

The article introduces some key issues associated with the understanding and representation of the emergence of behaviours in the architecture. When modelling complex systems, one of the characteristics is that behaviours can not be linear during time and dynamically evolve during the simulation. Evolution capabilities should then be given to agents when designing the system. Such evolution capabilities can be classified as:

• Evolution of knowledge, where agents compute internal parameters as needed, to satisfy their autonomous characteristic.

• Evolution of the agent's environment. This kind of evolution reflects the emergence of structures, so-called self-organisation.

• Evolution of the agent's behaviours during the simulation, decomposed in:
  • Adaptation of the behaviour according to knowledge and environment evolution. Adaptation is then defined as the capability of an organism to improve and adjust his behaviour to his environment. This property is so-called self-adaptation.

  • Description of non-predictable behaviours, where machine-learning techniques are used to observe the system and analyse the best actions. From the analysis of the best actions of the system, some rules can then be generalised. Such capabilities lead the system to prediction applications rather than modelling ones.

  • Emergence of global solutions, which could be associated with the first issue, that is adaptation of agent's behaviour to knowledge and environment. In addition, such adaptation should take care of the global objective of the multi-agent system.

The paper focusses on the first point - that is, the adaptation of behaviours according to knowledge and environment evolution. This adaptation is seen in GEAMAS as the result of interactions between agents and their environment. To give the architecture the means to display such a functionality, a specific message type, the "Recomposition", has been introduced. The Recomposition mechanism allows information transfer to higher levels in the architecture when instability of reactive agents is detected.

The paper is divided into two main parts. The next section presents the architecture composed of the society, cognitive and reactive agents. The following describes how complex behaviours occurs from the study of local interactions between reactive agents in such a context. Some elements of the implementation are briefly covered. Finally, the last section concludes the article by pointing out current investigations.