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Central European Exchange Program for University Studies

Presentation Title:  Multiagent optimization: Genetic Algorithms, Particle Swarm Optimization and Ant Colony

Lecturer:           MIKLOS POT,
Subotica Tech - College of Applied Sciences,
SERBIA

Time schedule 1:    09.04.2019      Tuesday         13:30-14:15
Place 1:                  room 423, floor 4, FMI

Time schedule 2:    11.04.2019      Thursday        13:30-17:45
Place 2:                  room 541, floor 5, FMI

Course Description:
In multiagent systems instead of one agent, a number of agents are searching for the solution simultaneously. These agents share the information available to help and speed up the route to the solution. Genetic algorithms (GA, Holland – 1975) take advantage of the genetic operators that occur in nature in the reproduction of the species. As in Darwinian theory, generations of individuals are evolving and improving using genetic operators such as selection, recombination and mutation. The quality of individuals is evaluated using the fitness function which describes how good the individual is for the problem being solved. Two examples will be presented:
How to find a maximum of a function, and How to solve the 8-queen problem.
Particle swarm optimization (PSO, Kennedy, Eberhart – 1995) describes the behavior of group of animals (birds, fish) that live in swarms. The swarm is moving together to find food easier and to escape from predators. The movement of each member (particle) of a swarm is influenced by three factors: the inertial component that forces the particle to keep its current direction, the cognitive component that forces the particle to return to positions where it had high fitness and the social component that forces the particle to move together with the swarm.
The PSO has some similarities with the GA such as the fitness function and the evolution of generations, but the information sharing mechanism is quite different. In PSO only the global best gives the information to the rest of the particles.
Ant Colony Optimization (ACO, Dorigo – 1992) describes the movement of ants as they search for food. As it can be observed in nature, after some time all ants follow the same trail which is the shortest path to food. This can be achieved because ants leave a pheromone trail. The shorter the trail, the more time it will be needed for the pheromone to evaporate. The ACO will be presented on the Traveling Salesman Problem (TSP).
Participation: FREE for students and lecturers of the FMI, Plovdiv University.  
For Information:    Assoc. Prof. Dr. Elena Somova
tel. 261 259; 0889 338997, e-mail: eledel@uni-plovdiv.bg

 

 

 

Central European Exchange Program for University Studies

Presentation Title: Gamification as the new paradigm

Lecturer:           PROF. DR. ROBERT PINTER, Subotica Tech - College of Applied Sciences, SERBIA

Time schedule 1:    09.04.2019      Tuesday         12:45-13:30
Place 1:                  room 423, floor 4, FMI

Time schedule 2:    11.04.2019      Thursday        9:15-13:30
Place 2:                  room 541, floor 5, FMI

Course Description:
In this presentation, we will highlight the most important options about playing games and the gamification. Our goal is to show that factors which are motivating us to play games are also applicable in the real world. For example, with gamification we can motivate employees to be more efficient, we can change people’s behavior or we can find a solution to the complex problem. The presentation consists of three parts. The first part is about the “power of the game”. In this part, we try to find an answer to the question “Why are we playing?”. In the second part, we will define the “gamification” phrase. We will also present elements and techniques required for implementing gamification into a software application. The third part is about presenting good examples of gamification.
Participation: FREE for students and lecturers of the Faculty of Mathematics and Informatics, Plovdiv University.
For Information:    Assoc. Prof. Dr. Elena Somova
tel. 261 259; 0889 338997, e-mail: eledel@uni-plovdiv.bg

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