20.500.12556/RUL-30856 Umetno življenje na primeru kolonije inteligentnih agentov Artificial life on a colony of intelligent agents Izdelali smo simulacijo umetnega življenja na primeru kolonije inteligentnih posameznikov (agentov), pri čemer smo uporabili kombinacijo nevronskih mrež, genetskih algoritmov in optimizacije s kolonijo mravelj. Vsak agent ima svoje možgane, implementirane z nevronsko mrežo. Večje število agentov tvori kolonijo, ki ima interakcijo z okoljem preko nabiranja in shranjevanja hrane, med seboj pa komunicirajo s pomočjo feromonskih sledi. Preko dedovanja in mutacije že obstoječih nevronskih mrež je omogočen razvoj kolonije. S simulacijo smo pridobili podatke o učinkovitosti kolonij z različnimi stopnjami mutacije (0,01 do 0,10) in rezultate med seboj primerjali. Kolonije z nizkimi stopnjami mutacije so bile v splošnem manj uspešne, kolonije z visokimi stopnjami mutacije pa so uspešno in hitro razvile le nekatera vedenja. Za najbolj uspešne so se izkazale kolonije s stopnjo mutacije 0,05, ki predstavlja dobro ravnovesje med kreiranjem novih agentov in kopiranjem že obstoječih uspešnih agentov. Simulacija omogoča spreminjanje parametrov in predstavlja dobro osnovo za nadaljnji razvoj in nadgradnje ter dodajanje bolj kompleksnih vedenj agentov. We developed a simulation of artificial life with a colony of intelligent individuals (agents) by using a combination of neural networks, genetic algorithms, and ant colony optimization. Each agent has its own brain implemented as a neural network. Several agents form a colony which interacts with the environment by gathering and storing food. They communicate using pheromone trails. Through the processes of inheritance and mutation of agents' brain the colony can develop continuously. With simulation we gathered the information on the effectiveness of colonies with varying rates of mutation (from 0.01 to 0.10) and compared the results. The colonies with low mutation rates were overall less successful, while the colonies with high mutation rates were successful in developing only certain behaviours. The most successful colonies used the mutation rate of 0.05, which presents a good balance between creating new agents and copying the existing successful agents. The simulation allows adjustment of parameters and presents a good basis for further development and adding more complex agent behaviours. umetno življenje nevronske mreže genetski algoritmi optimizacija s kolonijo mravelj artificial life neural networks genetic algorithms ant colony optimization true false false Slovenski jezik Angleški jezik Diplomsko delo 2015-07-02 14:20:00 2015-07-02 14:20:01 2022-08-01 11:17:46 0000-00-00 00:00:00 2015 0 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 1970-01-01 16801 Ritlop_Rok_-_Umetno_zivljenje_na_primeru_kolonije_inteligentnih_agentov.pdf Ritlop_Rok_-_Umetno_zivljenje_na_primeru_kolonije_inteligentnih_agentov.pdf 1 001D9912866E1C598A16B73411097469 c122b4f44f1889a35e865e954f2fcc54f135ff4dea0894edfe822b116cb15b5c 5479caa5-a1ad-11eb-a523-00155dcfd717 20.500.12556/rul/ceea3096-3b82-4599-9d93-c1403d21a377 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=30865 Fakulteta za računalništvo in informatiko 0 0 0