The aim of this course is to model and solve certain complex problems using so-called “collaborative” algorithms. These have the peculiarities of taking an example from nature (genetic algorithms, ant colonies, ..., neural networks) and of using the collective experience of "individuals" (agents) with weak capacities to make one. collective intelligence. For example, neural networks seek to mimic the brain's ability to solve a problem by using the multitude of neurons (each with poor resolving capacity) that make it up. The applications dealt with in progress are varied: character recognition, detection of outlines (in an image), resolution of a poker game (simplified) (or even other games), decoding of a text, search for a path the shortest (Dijkstra, traveling salesman), fault detection, bus allocation and Simultaneous Mapping and Localization by use of robots ...
multi-agents, robotics, genetic algorithms, ant colonies, neural networks, slam
- computer implementation of the proposed algorithms multi-agent modeling of complex problems
Final mark = 50% Knowledge + 50% Know-how Knowledge = final exam Know-how = continuous assessment