Pierre Marcenac

GEAMAS: A Generic Architecture for Agent-Oriented Simulations ofComplex Processes

This papers object is to present the results of the GEAMAS project which aims at modeling and simulating natural complex systems. GEAMAS is a generic architecture of agents used to study the behavior emergence in such systems. It is a multiagent program meant to develop simulation applications. Modeling complex systems requires to reduce, to organize the system complexity and to describe suitable components. Complexity of the system can then be tackled with an agent-oriented approach, where interactions lead to a global behavior. This approach helps in understanding how non-determinist behavior can emerge from interactions between agents, which is near of self-organized criticality used to explain natural phenomena. In the Applied Artificial Intelligence context, this paper presents an agent software architecture using a model of agent. This architecture is composed of three abstract levels over which the complexity is distributed and reduced. The architecture is implemented in ReActalk, an open agent-oriented development tool, which was developed on top of Smalltalk-80. To illustrate our purpose and to validate the architecture, a simulation program to help in predicting volcanic eruptions was investigated. This program was run over a period of one year and has given many satisfying results unattainable up to there with more classical approaches.

Towards an emergence machine for complex systems simulations

This paper presents a simulation platform that has been realised in Java 1.1 for the study of behaviour and evolutionary processes in non-linear systems. To support the modelling of such systems, we propose the use of agent technology as high level tool to design applications. The framework enables to study emergence by exploiting distributing computing as key issues of the system behaviour. Applications developed with the platform are then simulated to adequately capture any behaviour likely to be observed, to exhibit self organised structures, and to emphasise complex processes, which are brought in action. This approach then allows the study of macroscopic collections endowed with the potential to evolve during time.

MUTANT: a multiagent toolkit for artificial life simulation

Nowadays many artificial life research areas resort to agent-based simulation. This fact has brought us to design a powerful generic platform that would allow scientists in those fields of research to easily build simulation environment. This platform called MUTANT includes a model of self-adaptive agent with genetic evolving capabilities as learning mechanisms. The platform also includes a powerful graphical user interface providing many tools for both modeling and simulation. There are tools for agents design behaviors programming environments description and observation of running simulations. MUTANT is being developed in Java with the aim of being directly usable throughout the Internet.

Genetic Progamming for Automatic Design of Self-Adaptive Robots

The general framework tackled in this paper is the automatic generation of intelligent collective behaviors using genetic programming and reinforcement learning. We define a behavior-based system relying on automatic design process using artificial evolution to synthesize high level behaviors for autonomous agents. Behavioral strategies are described by tree-based structures, and manipulated by genetic evolving processes. Each strategy is dynamically evaluated during simulation, and weighted by an adaptative value. This value is a quality factor that reflects the relevance of a strategy as a good solution for the learning task. It is computed using heterogeneous reinforcement techniques associating immediate and delayed reinforcements as dynamic progress estimators. This work has been tested upon a canonical experimentation framework: the foraging robots problem. Simulations have been conducted and have produced some promising results.

GEAMAS V2.0: an object oriented platform for complex systems simulations

This paper presents the object oriented design and implementation of GEAMAS V2.0, a toolkit for virtual simulations of complex systems. GEAMAS V2.0 is structured in three modules: the kernel, the generation environment and the simulation environment. The kernel implements an object model for agents and provides generic classes. The generation environment allows the graphical design of applications. The simulation environment enables the observation of the simulations evolution via graphical user interface tools. The implementation uses Java 1.1. We applied GEAMAS V2.0 to the sand-pile automaton problem. This reference application, which is easily modeled with GEAMAS V2.0, provides a first validation of our system architecture and simulation mechanism.

Modeling multiagent systems as self-organized critical systems

The general framework of our project is to provide a computational model of physical complex processes for simulation needs. In geophysics, the study of this kind of system has led to the concept of self-organized criticality, to explain the repeatability of emergent phenomena in nature. To model self-organized criticality within computers, the original part of the work is to propose a multiagent platform where emergent phenomena are dynamically created during simulation at the time they occur. The aim of this paper is to show that multiagent systems, studied as emergent systems, can help in providing adequate mechanisms needed to model self-organization in complex systems. The paper introduces some key issues associated with the understanding of intrinsic mechanisms leading to self-organization and discusses the implementation of such mechanisms in an agent architecture. Finally, it describes a life-sized experimentation in earthquake simulation for validation.

Designing and implementing complex systems with agents

This paper describes an ongoing research in the Geomas project, initially intended to study applications of agent technology in complex systems. A complex system can be defined as a system in which behavior is bad-understood and designing such systems then requires specific considerations, justifying the need of the agent paradigm, when no other solutions could be found in an efficient way. The complex system tackled in this paper to illustrate our purposes is the prediction of volcano eruptions. Through the presentation of a simulation application for volcano phenomena, this paper focus on a software engineering approach to agent modelling in simulation. To address such issues, the paper describes an agent architecture through of as software engineering models of agents. A structural approach of the designing task is introduced by 1. conducting a top-down analysis to look for autonomous agents; 2. identifying internal behaviors, interaction processes and evolving facilities of each agent; and 3. looking at the emergence of the global behavior.

An Authoring System for ITS Which Is Based on a Generic Level of Tutoring Strategies

This paper presents the results of a research which took place at the University of Nice-Sophia Antipolis during the last four years and is now continued at the University of La Reunion. It particularly describes the architecture of an authoring system, EDDI, which aims at providing a basis for the development of Intelligent Tutoring Systems (ITS). The system is not general to any domain to teach, but is more adapted to domains in which knowledge is structured and domains which require a well-known expertise, such as diagnosis.

A framework to model multiple environments in multiagent systems

Nowadays, multiagent systems are very often used to run environmental simulations. Thanks to the fact than multiagents focus more on interactions rather than on the system in its globality, scientists and researchers are now able to represent complex systems and they can simulate them. Consequently, a large number of multiagents platforms allows to perform such kind of simulations. But there is no work on the fact that an agent can evolve simultaneously in multiple environments. This is why, in this paper, we present a very innovative architecture that allow to model multiple environment for a single agent in a multiagent system. But as this multiplicity of environments raises new problems with regard to classic architecture, we explain how, by adding new entities, we obtain a coherent and viable model. These new entities are notably the virtual environmental instance and the virtual environment. This new model is explained and detailed due to concrete examples and more particularly with an example on the movement of shoals of fish in Indian Ocean.

MUTANT: A Genetic Learning System

This paper presents MUTANT, a learning system for autonomous agents. MUTANT is an adaptive control architecture founded on genetic techniques and reinforcement learning. The system allows an agent to learn some complex tasks without requiring its designer to fully specify how they should be carried out. An agent behavior is defined by a set of rules, genetically encoded. The rules are evolved over time by a genetic algorithm to synthesize some new better rules according to their respective adaptive function, computed by progressive reinforcements. The system is validated through an experimentation in collective robotics.