Course: Ant Colony Optimization and Particle Swarm Optimization
Swarm optimization methods are inspired by the collective behavior of swarms. These methods have become an important part of the field of optimization and and are often the state-of-the-art methods for applications in areas like scheduling, vehicle routing, or bioinformatics.
In the fist part of tutorial this tutorial we suvey results on Ant Colony Optimization (ACO). ACO is inspired by the foraging behavior of ants and can be aplied to solve hard combinatorial optimization problems. In the second part of the tutorial we discuss Particle Swarm Optimization (PSO). PSO is inspired by the foraging behaviour of bird flocks and is mainly used for function optimization.
We first see why modal logics have been so successful to model various attitudes of agents (informational, motivational or social), and see the main concepts of the most famous BDI logic for Beliefs, Desires and Intentions. Then, zooming in on a specific modal logic, i.e. the logic of knowledge, we see how one easily obtains group notions of knowledge from the basic ones. We use the modal semantics to model some non-trivial MAS-scenarios in which knowledge plays a crucial role.