|
|
Shaping of Collective Intelligence in Cyberspace on the Example of Computer Games
Chaloupková, Šárka ; Marcelli, Miroslav (advisor) ; Charvát, Martin (referee)
The aim of the tehesis is to describe the formation of collective intelligence in cyberspace and focus on its use on the example of computer games. The idea is based on the works of Pierre Lévy, who believes that in the digital revolution is the value of the picture one of the most important. In this process plays an equally important role to increase the value of ideas, narratives, social communities and the development of new media tools and technologies. Along with that, in addition to the internal space of videogames (story progression, social interaction, building of the gaming space, gaming subculture, roleplay), shapes even the space beyond the game itself (transmedia storytelling, produsage - fan sites, content sharing, discussion forums). Thesis will be listed by concepts of collective intelligence, the progress of social memory, cyberspace and will be also inspired by other authors who works with collective intelligence, like Henry Jenkins, James Surowiecki or Derrick de Kerckhove. It should also provide an opposite view of the problem based on the work of opponents, which is eg. Cory Doctorow. Strategies and the use of the collective intelligence will be described by using the example of the specific games.
|
|
|
Swarm Intelligence
Winklerová, Zdenka ; Šaloun, Petr (referee) ; Škrinárová,, Jarmila (referee) ; Zbořil, František (advisor)
The intention of the dissertation is the applied research of the collective ( group ) ( swarm ) intelligence . To demonstrate the applicability of the collective intelligence, the Particle Swarm Optimization ( PSO ) algorithm has been studied in which the problem of the collective intelligence is transferred to mathematical optimization in which the particle swarm searches for a global optimum within the defined problem space, and the searching is controlled according to the pre-defined objective function which represents the solved problem. A new search strategy has been designed and experimentally tested in which the particles continuously adjust their behaviour according to the characteristics of the problem space, and it has been experimentally discovered how the impact of the objective function representing a solved problem manifests itself in the behaviour of the particles. The results of the experiments with the proposed search strategy have been compared to the results of the experiments with the reference version of the PSO algorithm. Experiments have shown that the classical reference solution, where the only condition is a stable trajectory along which the particle moves in the problem space, and where the influence of a control objective function is ultimately eliminated, may fail, and that the dynamic stability of the trajectory of the particle itself is not an indicator of the searching ability nor the convergence of the algorithm to the true global solution of the solved problem. A search strategy solution has been proposed in which the PSO algorithm regulates its stability by continuous adjustment of the particles behaviour to the characteristics of the problem space. The proposed algorithm influenced the evolution of the searching of the problem space, so that the probability of the successful problem solution increased.
|
guest ::
