| dc.contributor.advisor | Downing, Keith | nb_NO |
| dc.contributor.author | Mathisen, Bjørn Magnus | nb_NO |
| dc.date.accessioned | 2014-12-19T13:33:33Z | |
| dc.date.available | 2014-12-19T13:33:33Z | |
| dc.date.created | 2010-09-04 | nb_NO |
| dc.date.issued | 2007 | nb_NO |
| dc.identifier | 348449 | nb_NO |
| dc.identifier | ntnudaim:1221 | nb_NO |
| dc.identifier.uri | http://hdl.handle.net/11250/251163 | |
| dc.description.abstract | This thesis presents a further development of Neuroevolution of Augmenting topologies(NEAT)[21]. The author augments NEAT by parallelizing the fitness evaluation of the phenotypes enabling the method to be utilized on highly complex fitness evaluations by running it on a cluster. This augmented version of NEAT is then applied to the inherently complex problem of the Go board game, by using the Gnugo (See www.gnu.org/software/gnugo/.) software package as a fitness evaluator. The performance increase also enables the author to follow up on the predictions of Kenneth Stanley s previous discussions that co-evolution will help evolve a more general Go player, rather than the predicted evolved behaviour of specializing in beating Gnugo. | nb_NO |
| dc.language | eng | nb_NO |
| dc.publisher | Institutt for datateknikk og informasjonsvitenskap | nb_NO |
| dc.subject | ntnudaim | no_NO |
| dc.subject | MIT informatikk | no_NO |
| dc.subject | Kunstig intelligens og læring | no_NO |
| dc.title | Evolving a roving-eye for go revisited | nb_NO |
| dc.type | Master thesis | nb_NO |
| dc.source.pagenumber | 88 | nb_NO |
| dc.contributor.department | Norges teknisk-naturvitenskapelige universitet, Fakultet for informasjonsteknologi, matematikk og elektroteknikk, Institutt for datateknikk og informasjonsvitenskap | nb_NO |
