Uncertainty-based decision support system for gaming applications

Autor: Vinayak Jagtap, Parag Kulkarni, Pallavi Joshi
Rok vydání: 2023
Předmět:
Zdroj: Journal of Intelligent & Fuzzy Systems. 44:3381-3397
ISSN: 1875-8967
1064-1246
Popis: A dynamic world has different uncertainties. These uncertainties always impact adversely while making decisions. Existing systems sometimes fail as they are trained without considering uncertainty inclusion due to the dynamic nature of the problem. This is quite observed in gaming, which is most dynamic and contributes adversely while deciding for the next move. Strategic games have fewer uncertainties rather than ground sports. Many types of factors add uncertainty to the system. There is a need of handling the required uncertainty which will help in making the decision. Also while finding similarities between games or matches, player and playing style results don’t depict exact similarities between them. There is a need to measure uncertainty-based similarities as it helps in deciding the situation of the game or player. Here Uncertainty based decision support system is proposed which takes uncertainty as input rather than only considering patterns of input. Patterns always help if the system is more static while considering a dynamic system where we need to consider patterns and uncertainties in the scenarios. Results are shown on limited types of moves in game data and how uncertainty-based similarity and next move selection are improved.
Databáze: OpenAIRE
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