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Evolving Neuromodulatory Topologies for Plasticity in Video Game Playing
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In the last decades neural networks have become more frequent in video games. Neuroevolution helps us generate optimal network topologies for specific tasks, but there are still still unexplored areas of neuroevolution, and ways of improving the performance of neural networks, which we could utilize for video game playing. The aim of this thesis is to find a suitable fitness evaluation and improve the plasticity of evolved neural networks, as well as comparing the performance and general video game playing abilities of established neuroevolution methods. Using Analog Genetic Encoding we implement evolving neuromodulatory topologies in a typical two-dimensional platformer video game, and have it play against itself without neuromodulation, and against a popular genetic algorithm known as Neuroevolution of Augmenting Topologies. A suitable input and output structure is developed as well as an appropriate fitness evaluation for properly mating and mutating a population of neural networks. The benefits of neuromodulation are tested by running and completing a number of tile-based platformer video game levels. The results show an increased performance in networks influenced by neuromodulators, but no general video game playing abilities are obtained. This shows us that a more advanced general gameplay learning method with higher influence is required.

Abstract [sv]

Neurala nätverk har blivit allt vanligare i tv-spel. Neuroevolution hjälper oss att utveckla optimala neurala topologier för specifika uppgifter, men det finns fortfarande outforskade områden i neuroevolution, och sätt att förbättra förmågan hos neurala nätverk som vi kan använda i spel. Målet är att hitta en lämplig fitnessbedömning och förbättra plasticiteten hos utvecklade neuralanätverk, samt jämföra deras utförande och förmåga att generellt spela videospel. Detta med hjälp av etablerade neuroevolutionmetoder. Genom Analog Genetisk Kodning implementeras utvecklande neuromodulatoriska topologier i ett typiskt tvådimensionellt platformer spel. Det används sedan för att spela mot en version av sig själv som inte har neuromodulatoriska egenskaper, samt mot en populär genetisk algoritm kallad Neuroevolution of Augmenting Topologies. Ett passande format för input och output, samt en fitnessbedömningsmetod för parande och muterande aven population av neurala nätverk utvecklas. Fördelarna med neuromodulation testas genom att låta nätverken spela ett antal tile-baserade platformerbanor. Resultaten visar en förbättring av utförandet hos nätverk som utvecklat neuromodulatorer, dock inga generella spelkunskaper kunde läras. Detta visar oss att en mer avancerad metod för generellt spelande krävs för att kunna få ett neuralt nätverk kunna spela och lösa mer generella problem.

Place, publisher, year, edition, pages
2016.
Keyword [en]
Neuromodulation, Neural Networks, Neuroevolution, Lifelong Learning
Keyword [sv]
Neuromodulation, Neurala Nätverk, Neuroevolution, livslångt lärande
National Category
Software Engineering Computer Science
Identifiers
URN: urn:nbn:se:bth-12888OAI: oai:DiVA.org:bth-12888DiVA: diva2:948213
Subject / course
Degree Project in Master of Science in Engineering 30.0
Educational program
PAACI Master of Science in Game and Software Engineering
Presentation
2016-06-02, 15:32 (English)
Supervisors
Examiners
Available from: 2016-07-12 Created: 2016-07-09 Last updated: 2016-07-12Bibliographically approved

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fulltext(1207 kB)127 downloads
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CiteExportLink to record
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Citation style
  • apa
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