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Developed within the RAGE project funded by EU within Horizon2020 program. This asset enables a real-time automatic adaptation of game difficulty to player's expertise level. The adaptation algorithm makes use of a stealth assessment algorithm that assigns difficulty ratings and expertise ratings to the players and the game modules respectively. The asset tracks changes in these ratings allowing assessment of players' learning progress either by players themselves or by instructors. This is the version written in C# language. Version 1.2 includes considerable extensions to the TwoA functionalities: • API for building scenario dependency graphs • An improved scenario selection algorithm • The second module for adaptation and assessment based on continuous accuracy only • Extended parameter setting API
|Place of Publication||Heerlen|
|Publisher||Open Universiteit Nederland|
|Publication status||Published - 23 Mar 2017|
- automatic game adaptation
- stealth assessment
- adaptation algorithm
FingerprintDive into the research topics of 'Adaptation and Assessment (TwoA) asset in C# (v1.2)'. Together they form a unique fingerprint.
- 1 Finished
Rage: Realising an Applied Gaming Eco-system
Westera, W., Georgiadis, K., Saveski, G., van Lankveld, G., Bahreini, K., van der Vegt, W., Berkhout, J., Nyamsuren, E., Kluijfhout, E. & Nadolski, R.
1/02/15 → 31/07/19