Research
Main topics
My research topics belong to the field of Swarm Intelligence and Complex Systems, addressing both theoretical and applied aspects :
- Synthesis of Artificial Complex Systems
- Collective motion mechanisms
- Self-organization by synchronization mechanisms
- Adaptation by control mechanisms
- Nonlinear dynamics
Application domains :
- Flocking simulations
- Flocks in Robotics
- New metaheuristics for optimization
Abstract of my PhD thesis
Swarm Intelligence is from now on a full part of Distributed Artificial Intelligence. Its associated problematics meet many other fields and scientific questions. The concept of swarm in particular belongs to the science called the science of complex systems. This PhD thesis shows the design and characteristics of a novel type of model called the logistic multi-agent system (LMAS) dedicated to Swarm Intelligence. The LMAS has its foundations in complex system modeling: it is inspired from the coupled logistic map lattice model which has been adapted to the ``Influence-Reaction'' modeling of multi-agent systems. This model is based on universal principles such as synchronization and parametric control which are considered as the main mechanisms of self-organization and adaptation at the heart of the system. The field-layered based environment is the other important feature of the LMAS, since it enables indirect interactions and plays the part of a data structure for the whole system. The work of this thesis is put into practice for simulation and optimization. The novelty of the LMAS lies in its generic theoretical framework, which enables to tackle problems considered as distinct in the literature, in particular flocking and ant-like stigmergic behavior. This model meets the need of explaining basic mechanisms and the need of synthesizing generative algorithms for Swarm Intelligence.
The Logistic Multi-Agent System
Some slides summarizing this model:
(in french):Thesis_slides.pdf
Summary of my contributions
The interest and the novelty of the logistic multi-agent system lies in its general framework to model distinct phenomena such as collective motion (flocking behaviors) or stigmergic mechanisms based on pheromone deposits (in particular ant foraging). This model leads moreover to explain different types of behaviors by means of dynamical analysis according to a mechanist philosophy. We have shown in that way that :
- Collective motion as an instance of self-organization is caused by internal synchronization between the agent states.
- Stigmergic mechanisms are achieved by a decentralized control within agents which is governed by the amount of pheromone perceived in the environment.
The deterministic dynamical system theory which the logistic multi-agent system is based on, has some advantages:
- It provides computational quantities to follow the dynamics such as Lyapunov coefficient or Kolmogorov entropy. These quantities may be aproximated as the work on flocking simulations have shown.
- It can generate stochastic behaviors without probabilities, which is needed for the exploration phases for optimization problems, by means of chaotic dynamics.
- The deterministic aspect of the dynamics enables to monitor the behavior of the system to a given extent and to predict its future qualitatively.
The logistic multi-agent system is therefore a new approach in the Swarm Intelligence field since all other existing algorithms --to our knowledge-- are based on stochastic approaches.
Finally in terms of applications to classical problems in computer science, the metaheuristic aspect of the logistic multi-agent system (LMAS) for optimization has been tested and compared to exiting metaheuristics. Without being more powerful at this level of development, the LMAS - renamed in this context as the algorithm of logistic ants – obtains very promising results, comparable to the first versions of ACO (Ant Colony Optimization) on small TSP instances. Due to its determinist characteristics it also sheds a new light on the involved mechanisms. The convergence principle of the algorithm is rather similar to the well-known simulated annealing algorithm.
In conclusion I have proposed an innovative modeling approach that is completely deterministic involving nonlinear decisional processes into Swarm Intelligence.