TY - GEN
T1 - New adaptations for evolutionary algorithm applied to dynamic difficulty adjustment system for serious game
AU - Lach, Ewa
N1 - Publisher Copyright:
© 2018, Springer International Publishing AG.
PY - 2018
Y1 - 2018
N2 - The aim of the Dynamic Difficulty Adjustment is to dynamically balance the difficulty level of the games in order to keep the user interested in playing. Generally, a game in which the challenge level matches the skill of the human player has a greater entertainment value than a game that is either too easy (boring) or too hard (frustrating). An entertainment has an important role to play in serious games (educational games), contributing to their motivational and engaging qualities leading to players voluntarily playing serious games for extended periods of time. In this paper, we present new adaptations for reducing the number of training data for the evolutionary algorithms used to find game settings suitable for the player of a serious game. The training process for a human player should be as short as possible. Various experiments are performed. The obtained results show that the proposed adaptation causes a substantial decrease in training data for different players.
AB - The aim of the Dynamic Difficulty Adjustment is to dynamically balance the difficulty level of the games in order to keep the user interested in playing. Generally, a game in which the challenge level matches the skill of the human player has a greater entertainment value than a game that is either too easy (boring) or too hard (frustrating). An entertainment has an important role to play in serious games (educational games), contributing to their motivational and engaging qualities leading to players voluntarily playing serious games for extended periods of time. In this paper, we present new adaptations for reducing the number of training data for the evolutionary algorithms used to find game settings suitable for the player of a serious game. The training process for a human player should be as short as possible. Various experiments are performed. The obtained results show that the proposed adaptation causes a substantial decrease in training data for different players.
KW - Dynamic Difficulty Adjustment (DDA)
KW - Evolutionary algorithm
KW - Game AI
KW - Serious game
UR - https://www.scopus.com/pages/publications/85030760914
U2 - 10.1007/978-3-319-67792-7_48
DO - 10.1007/978-3-319-67792-7_48
M3 - Conference contribution
AN - SCOPUS:85030760914
SN - 9783319677910
T3 - Advances in Intelligent Systems and Computing
SP - 492
EP - 501
BT - Man-Machine Interactions 5 - 5th International Conference on Man-Machine Interactions, ICMMI 2017
A2 - Gruca, Aleksandra
A2 - Czachorski, Tadeusz
A2 - Harezlak, Katarzyna
A2 - Kozielski, Stanislaw
A2 - Piotrowska, Agnieszka
A2 - Czachorski, Tadeusz
PB - Springer Verlag
T2 - 5th International Conference on Man-Machine Interactions, ICMMI 2017
Y2 - 3 October 2017 through 6 October 2017
ER -