Star Vault AB is a video game development company that has developed the video game Mortal Online. The company has stated that they believe that players new to the game repeatedly find themselves being lost in the game. The objective of this study is to evaluate whether or not an Artificial Neural Network can be used to evaluate when a player is lost in the game Mortal Online. This is done using the free open source library Fast Artifical Neural Network Library. People are invited to a data collection event where they play a tweaked version of the game to facilitate data collection. Players specify whether they are lost or not and the data collected is flagged accordingly. The collected data is then prepared with different parameters to be used when training multiple Artificial Neural Networks. When creating an Artificial Neural Network there exists several parameters which have an impact on its performance. Performance is defined as the balance of high prediction accuracy against low false positive rate. These parameters vary depending on the purpose of the Artificial Neural Network. A quantitative approach is followed where these parameters are varied to investigate which values result in the Artificial Neural Network which best identifies when a player is lost. The parameters are grouped into stages where all combinations of parameter values within each stage are evaluated to reduce the amount of Artificial Neural Networks which have to be trained, with the best performing parameters of each stage being used in subsequent stages. The result is a set of values for the parameters that are considered as ideal as possible. These parameter values are then altered one at a time to verify that they are ideal. The results show that a set of parameters exist which can optimize the Artificial Neural Network model to identify when a player is lost, however not with the high performance that was hoped for. It is theorized that the ambiguity of the word "lost" and the complexity of the game are critical to the low performance.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:bth-14812 |
| Date | January 2017 |
| Creators | Bergsten, John, Öhman, Konrad |
| Publisher | Blekinge Tekniska Högskola, Institutionen för kreativa teknologier |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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