Carole Bernon

Agent-oriented software engineering

Considering the great number of agent-oriented methodologies that can be found in the literature, and the fact that each one defines its own concepts and system structure, one of the main challenges in agent-oriented software engineering (AOSE) research is how to make these methodologies interoperable. By defining concepts used in a specific domain in a non-ambiguous way, meta-modelling may represent a step towards such interoperability. Consequently the main objective of the AOSE TFG (Technical Forum Group) is to establish a strategy for identifying a common meta-model that could be widely adopted by the AOSE community. This paper sums up the approach used by this TFG which consists of (i) studying and comparing the meta-models related to some existing methodologies (ADELFE, Gaia, INGENIAS, PASSI, RICA and Tropos) in order to find commonalities and (ii) giving a clear and basic definition for the core concepts used in multi-agent systems for relating and positioning them in a unified MAS meta-model. The first proposal, set up by the working group, for this unified meta-model then concludes this paper.

Testing in multi-agent systems

Testing software agents and Multi-Agent Systems (MAS) needs suitable techniques to evaluate agents autonomous behaviours as well as distribution, social and deliberative properties, which are particular to these systems. This paper reviews testing methods and techniques with respect to the MAS properties they are able to address. For this purpose, we provide a reference framework that provides a classification of MAS testing levels (such as unit, agent, integration, system, and acceptance) and of testing approaches along the development artefact they exploit (namely, design and code artefacts). Open issues in testing MAS are then discussed providing a basis for a research roadmap.

Engineering Self-modeling Systems: Application to Biology

Complexity of todays systems prevents designers from knowing everything about them and makes engineering them a difficult task for which classical engineering approaches are no longer valid. Such a challenge is especially encountered in actual complex systems simulation in which underlying computational model is very tough to design. A prospective solution is to unburden designers as much as possible by letting this computational model self-build. Adaptive multi-agent systems are the foundation of the four-layer agent model proposed here for endowing systems with the ability to self-tune, self-organize and self-assemble. This agent model has been applied to an application (MicroMega) related to computational biology which aim is to model the functional behavior of unicellular yeast Saccharomyces Cerevisiae.

Controlling Bioprocesses Using Cooperative Self-organizing Agents

This paper presents an Adaptive Multi-Agent System (AMAS) to deal with the control of complex systems, such as bioprocesses, toward user-defined objectives. This control is made under a double constraint: no model of the controlled system can be used and the information available is limited to the values of the observable variables. Thanks to their observations, agents of the AMAS self-organize and create an adequate control policy to lead the system toward its objectives. The developed system is described and then tested on examples extracted from a prey-predator problem. Finally, the results are detailed and discussed.

A Multi-agent System for Autonomous Control of Game Parameters

Control of game parameters to reach domain-related objectives cannot be easily handled with classic control theory approaches. Given the dynamics and complexity of modern game engines, diversity of human players and their constantly changing nature, this paper advocates for means to tune game parameters in real time, with no use of game or users models. The proposed approach, based on a multi-agent system, is used to control two dynamic systems before analyzing the results.

Power Optimization by Cooling Photovoltaic Plants as a Dynamic Self-adaptive Regulation Problem

This paper shows an approach to control cooling devices for photovoltaic plants in order to optimize the energy production thanks to a limited reserve of harvested rainwater. This is a complex problem, considering the dynamic environment and the interdependence of the parameters, such as the weather data and the state of the photovoltaic panels. Our claim is to design a system composed of autonomous components cooperating in order to obtain an emergent efficient control.

A Cooperative Multi-Agent System for Wind Power Forecasting

In the coming years, ensuring the electricity supply will be one of the most important world challenges. Renewable energies, in particular wind energy, are an alternative to non-sustainable resources thanks to their almost unlimited supply. However, the chaotic nature and the variability of the wind represent a significant barrier to a large-scale development of this energy. Consequently, providing accurate wind power forecasts is a crucial challenge. This paper presents AMAWind, a multi-agent system dedicated to wind power forecasting based on a cooperative approach. Each agent corresponds to a turbine at a given hour, it starts from an initial production forecast and acts in a cooperative way with its neighbors to find an equilibrium on conflicting values. An assessment of this approach was carried out on data coming from a real wind farm.

Speeding up the Search of a Global Dynamic Equilibrium from a Local Cooperative Decision

Systems composed of many interdependent active entities working on shared resources can be challenging to regulate and multi-agent simulation is an efecient means for ending the suitable entities behaviors. On the other hand, the search space for a stable solution of the system is usually forbidding and hinders any effort to solve the problem using a top-down approach. Furthermore, the complexity of possible global functions to optimize increases rapidly with the size of the system and can prove difecult to deene and/or evaluate at each system simulation step. The difeculty when designing bottom-up systems is to be able to identify all their emergent properties and the parameters to modulate them. Here we propose a local cooperative decision making process that helps to stabilize such systems. These local processes prove to be very efecient to quickly end dynamic equilibrium solutions where the system continues to function and fulells its global function. Regulation emerges from simple local interactions.

Towards a guide for engineering the collective behaviour of a MAS

Abstract The objective of the work undertaken here is to endow an agent-oriented methodology with a semi-automatic tool for helping designers when drawing up the agents composing an adaptive multi-agent system (AMAS). This tool acts as a guide for enabling designers to influence the emergent global behaviour of an AMAS by acting on the local behaviour of its cooperative agents. The preliminary approach proposed in this article can be seen as a feasibility study aiming at developing a textual guide by considering the principles of the AMAS theory. Simulation of the behaviour of healthy and cancerous cells is used as a base for this study.