Abstract :
The tutorial starts with a presentation of the notion of complexity discussed within the fields of Artificial Intelligence, Neural Networks, Chaos Theory, Self-Organization, Non-linear systems, Emergence and Collective Intelligence. Examples of complex systems include ant-hills, human economies, climate, nervous systems, cells and living things, human beings, as well as modern telecommunication infrastructures, where simple units together behave in complicated ways. Swarm intelligence, which represents an artificial intelligence technique based on the study of collective behavior in self-organized systems, i.e., collective intelligence of groups of simple agents, is also discussed, including the techniques of Ant Colony Optimization and Particle Swarm Optimization. The discussion then encompasses Multi-agent systems, systems that consist of multiple agents or vehicles with several sensors/actuators and the capability to communicate with one another to perform coordinated tasks. The Consensus problem is presented in detail, on the basis of the concepts of Graph theory. Modeling of bird flocking is presented as an example. Decentralized control of autonomous vehicles is taken as another example, with an accent on Formation Control and Sensor Networks. A detailed discussion covers the vehicle and multi-robot formation stability problem, as well as decentralized estimation and control problems in Multi-agent systems based the implementation of a consensus strategy.
Keywords :
"Artificial intelligence","Remotely operated vehicles","Humans","Particle swarm optimization","Intelligent sensors","Multiagent systems","Biological neural networks","Artificial neural networks","Chaotic communication","Intelligent networks"
