Improved balance in multiplayer online battle arena games
| Autor: | Stefan D. Bruda, Chailong Huang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Battle neural network Computer science business.industry media_common.quotation_subject Geography Planning and Development Internet privacy ComputingMilieux_PERSONALCOMPUTING QA75.5-76.95 matching system game balance 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Electronic computers. Computer science mupliplayer online battle arena game 62m45 Improved balance business 030217 neurology & neurosurgery clustering 62h30 media_common |
| Zdroj: | Acta Universitatis Sapientiae: Informatica, Vol 12, Iss 2, Pp 183-204 (2020) |
| ISSN: | 2066-7760 |
| DOI: | 10.2478/ausi-2020-0011 |
| Popis: | The Multiplayer Online Battle Arena (MOBA) game is a popular type for its competition between players. Due to the high complexity, balance is the most important factor to secure a fair competitive environment. The common way to achieve dynamic data balance is by constant updates. The traditional method of finding unbalanced factors is mostly based on professional tournaments, a small minority of all the games and not real time. We develop an evaluation system for the DOTA2 based on big data with clustering analysis, neural networks, and a small-scale data collection as a sample. We then provide an ideal matching system based on the Elo rating system and an evaluation system to encourage players to try more different heroes for a diversified game environment and more data supply, which makes for a virtuous circle in the evaluation system. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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