This paper presents FEFFuL, an architecture used to estimate the fitness value of a generated artifact in any Evolution Strategy (ES) system that would otherwise require human evaluation, i.e.: Interactive Evolutionary Computation (IEC) systems. By learning directly human preferences, the FEFFuL network aims to reduce user's fatigue to a minimum while also adapting to new emergent artifacts. We apply here FEFFuL in the context of evaluating generated structures in the popular game Minecraft.
FEFFuL: A Few-Examples Fitness Function Learner / Brandizzi, N.; Fanti, A.; Gallotta, R.; Napoli, C.. - 3092:(2021), pp. 75-79. (Intervento presentato al convegno 2021 Scholar's Yearly Symposium of Technology, Engineering and Mathematics, SYSTEM 2021 tenutosi a Catania; Italia).
FEFFuL: A Few-Examples Fitness Function Learner
Brandizzi N.
Investigation
;Fanti A.Software
;Napoli C.Supervision
2021
Abstract
This paper presents FEFFuL, an architecture used to estimate the fitness value of a generated artifact in any Evolution Strategy (ES) system that would otherwise require human evaluation, i.e.: Interactive Evolutionary Computation (IEC) systems. By learning directly human preferences, the FEFFuL network aims to reduce user's fatigue to a minimum while also adapting to new emergent artifacts. We apply here FEFFuL in the context of evaluating generated structures in the popular game Minecraft.| File | Dimensione | Formato | |
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Brandizzi_FEFFuL_2021.pdf
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Note: http://ceur-ws.org/Vol-3092/p12.pdf
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2.53 MB | Adobe PDF |
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