Vi utbildar för framtiden och skapar samhällsnytta genom vår forskning som levandegörs i nära samarbete med näringslivet. Vi bedriver forskning inom computer science, datateknik, software engineering och interaktionsdesign - från grundforskning till direkta tillämpningar. Institutionen har en stark internationell prägel och är delad mellan Chalmers och Göteborgs universitet.
We are engaged in research and education across the full spectrum of computer science, computer engineering, software engineering, and interaction design, from foundations to applications. We educate for the future, conduct research with high international visibility, and create societal benefits through close cooperation with businesses and industry. The department is joint between Chalmers and the University of Gothenburg.
(2021) ATTERFORS, JOHAN; HOLMBERG, CARL; HUANG, ALEXANDER; KARHU, ROBIN; LAE, BRAGE; LARSSON VAHLBERG, ALEXANDER; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Dodig Crnkovic, Gordana; Fratarcangeli, Marco
As ecosystems are complex domains, both analytical and computer-aided models
can aid in gaining insights about their dynamics. One such computer-aided model
is the concept of ecosystem simulation. This project aims to build an interactive
and visual ecosystem simulation in the Unity game engine. The purpose is to explore how modelling of animal behavior, trait evolution and dynamic terrain can be
combined with a graphical representation to create an interactive ecosystem simulator. Implementation of these aspects includes exploration of machine learning and
reactive behavior for animals, terrain generation, genetic reproductive algorithms as
well as run-time visualization and collection of data. The effects of these aspects are
evaluated using comparisons between animal behavior models, impact of terrain and
outcomes of genetic evolution, in addition to software interactivity. The outcome
of this project indicated that the machine learning prioritization animals performed
nearly as well as reactive rule based animals in terms of survival, while the machine
learning steered animals performed sub-par in comparison to the others. Furthermore, it showed that terrain changes seemingly has a greater impact on the predator
populations compared to the prey populations in the simulator. Additionally, as a
result of the proposed evolution model, genetic traits of animals indicated to be
potentially adaptive to the environment. Finally, the graphical representation provided visual feedback and information to users. In total, the final product resulted
in a working interactive ecosystem simulator. The implications of this thesis offers a
baseline framework for modelling a visual interactive ecosystem simulator in regards
to future research, academic and entertainment applications.