A5018423883- Multi-Agent Systems and Negotiation
- Cellular Automata and Applications
- Simulation Techniques and Applications
- Evolutionary Algorithms and Applications
- Intelligent Tutoring Systems and Adaptive Learning
- Open Education and E-Learning
- Scientific Computing and Data Management
- Distributed and Parallel Computing Systems
- Semantic Web and Ontologies
- Reinforcement Learning in Robotics
- Information Technology and Learning
- Advanced Data Storage Technologies
- Evolutionary Game Theory and Cooperation
- Machine Learning and Data Classification
- Business Process Modeling and Analysis
- Business Strategy and Innovation
- Educational Tools and Methods
- Modular Robots and Swarm Intelligence
- Logic, Reasoning, and Knowledge
- European Monetary and Fiscal Policies
- AI-based Problem Solving and Planning
- Diverse multidisciplinary academic research
- Scheduling and Optimization Algorithms
- Mobile Agent-Based Network Management
- Transportation and Mobility Innovations
Google (United States)
2024
Observatoire des Sciences de l'Univers de la Réunion
1999-2003
Écologie Marine Tropicale des Océans Pacifique et Indien
1996-2002
Département Mathématiques et Informatique Appliquées
1994-1999
University of Reunion Island
1992-1998
Data is a critical resource for Machine Learning (ML), yet working with data remains key friction point. This paper introduces Croissant, metadata format datasets that simplifies how used by ML tools and frameworks. Croissant makes more discoverable, portable interoperable, thereby addressing significant challenges in management responsible AI. already supported several popular dataset repositories, spanning hundreds of thousands datasets, ready to be loaded into the most
Nowadays many artificial life research areas resort to agent-based simulation. This fact has brought us design a powerful generic platform that would allow scientists in those fields of easily build simulation environment. called MUTANT includes model self-adaptive agent with genetic evolving capabilities as learning mechanisms. The also graphical user interface providing tools for both modeling and There are agents behavior's programming environment's description observation running...
Data is a critical resource for Machine Learning (ML), yet working with data remains key friction point. This paper introduces Croissant, metadata format datasets that simplifies how used by ML tools and frameworks. Croissant makes more discoverable, portable interoperable, thereby addressing significant challenges in management responsible AI. already supported several popular dataset repositories, spanning hundreds of thousands datasets, ready to be loaded into the most
This paper presents the object oriented design and implementation of GEAMAS V2.0, a toolkit for virtual simulations complex systems. V2.0 is structured in three modules: kernel, generation environment simulation environment. The kernel implements an model agents provides generic classes. allows graphical applications. enables observation simulation's evolution via user interface tools. uses Java 1.1. We applied to sand-pile automaton problem. reference application, which easily modeled with...
The general framework of our project is to provide a computational model physical complex processes for simulation needs. In geophysics, the study this kind system has led concept self-organized criticality, explain repeatability emergent phenomena in nature. To criticality within computers, original part work propose multiagent platform where are dynamically created during at time they occur. aim paper show that systems, studied as can help providing adequate mechanisms needed...
Complex systems can be defined as with behavior that is poorly understood. These appear in a large variety of fields, such natural phenomena. Modeling phenomena an interesting challenge because phenomena, earthquakes, hurricanes and volcanoes, cause severe damage. What's more, this area, most classical models have failed both to understand the underlying physical processes predict future behavior. Our approach consider macro-behavior (i.e., result program execution) set interactions among...
This paper presents an interesting architecture to model the environment in multiagent simulations. The originality of our approach is that unlike majority systems, we do not perform tasks dedicated and together same entity. why separate agent into two entities: brain charge reasoning, body perceptions actions environment. We then propose a mechanism which decreases complexity For that, define new notions: porosity aggregation operation. applied this framework motion shoal swordfish.
GEAMAS is a knowledge engineering environment for multi-agent simulation of complex systems. The basic architecture designed around three dimensions: MultiAgent Systems (MAS) software design, MAS abstraction and services dimension. Each one this aspects, implemented into several modular open layers. First, the paper argues benefits such environment. We especially present: 1) how design dimension enables to define appropriate tools levels designing systems; 2) adds significant value implement...
Notre objectif est de construire un modèle l'apprenant avec une approche multi-agents. Les connaissances sont représentées par plusieurs niveaux granularité. À chaque type connaissance correspond d'agent. La description interne, l'organisation dans des graphes raisonnement, ainsi que la modification dynamique ces agents au cours résolution problèmes décrits première partie cet article. Par ailleurs, lorsque effectue raisonnement plus complexe, mettant en jeu agents, ce reconnu comme...
The general framework tackled in this paper is the automatic generation of intelligent collective behaviors using genetic programming and reinforcement teaming. We define a behavior-based system relying on design process artificial evolution to synthesize high level for autonomous agents. Behavioral strategies are described by tree-based structures, manipulated generic evolving processes. Each strategy dynamically evaluated during simulation, weighted an adaptation function as quality factor...
Nowadays, multiagent systems are very often used to run environmental simulations. These simulations need use real information. Unfortunately organizations (or companies), which can be everywhere in the world, hold these This fact has brought us build a generic platform that is able dynamically get distributed information throughout Internet. two different network protocols: UDP and RMI technology provided by Java. We explain this paper how implemented we give some benchmarks order compare...