Dynamic Difficulty Balancing for Cautious Players and Risk Takers
Dynamic balancing of game difficulty can help cater for different levels of ability in players. However, performance in some game tasks depends on not only the player's ability but also their desire to take risk. Taking or avoiding risk can offer players its own reward in a game situation. Furt...
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | International Journal of Computer Games Technology |
| Online Access: | http://dx.doi.org/10.1155/2012/625476 |
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| _version_ | 1849395465422897152 |
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| author | Guy Hawkins Keith Nesbitt Scott Brown |
| author_facet | Guy Hawkins Keith Nesbitt Scott Brown |
| author_sort | Guy Hawkins |
| collection | DOAJ |
| description | Dynamic balancing of game difficulty can help cater for different levels of ability in players. However, performance in some game tasks depends on not only the player's ability but also their desire to take risk. Taking or avoiding risk can offer players its own reward in a game situation. Furthermore, a game designer may want to adjust the mechanics differently for a risky, high ability player, as opposed to a risky, low ability player. In this work, we describe a novel modelling technique known as particle filtering which can be used to model various levels of player ability while also considering the player's risk profile. We demonstrate this technique by developing a game challenge where players are required to make a decision between a number of possible alternatives where only a single alternative is correct. Risky players respond faster but with more likelihood of failure. Cautious players wait longer for more evidence, increasing their likelihood of success, but at the expense of game time. By gathering empirical data for the player's response time and accuracy, we develop particle filter models. These models can then be used in real-time to categorise players into different ability and risk-taking levels. |
| format | Article |
| id | doaj-art-a94d054c2e39445f949d4bd3334c3bf0 |
| institution | Kabale University |
| issn | 1687-7047 1687-7055 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Computer Games Technology |
| spelling | doaj-art-a94d054c2e39445f949d4bd3334c3bf02025-08-20T03:39:36ZengWileyInternational Journal of Computer Games Technology1687-70471687-70552012-01-01201210.1155/2012/625476625476Dynamic Difficulty Balancing for Cautious Players and Risk TakersGuy Hawkins0Keith Nesbitt1Scott Brown2School of Psychology, Newcastle Cognition Laboratory, University of Newcastle, Callaghan, NSW 2308, AustraliaSchool of Design Communication and IT, University of Newcastle, Callaghan, NSW 2308, AustraliaSchool of Psychology, Newcastle Cognition Laboratory, University of Newcastle, Callaghan, NSW 2308, AustraliaDynamic balancing of game difficulty can help cater for different levels of ability in players. However, performance in some game tasks depends on not only the player's ability but also their desire to take risk. Taking or avoiding risk can offer players its own reward in a game situation. Furthermore, a game designer may want to adjust the mechanics differently for a risky, high ability player, as opposed to a risky, low ability player. In this work, we describe a novel modelling technique known as particle filtering which can be used to model various levels of player ability while also considering the player's risk profile. We demonstrate this technique by developing a game challenge where players are required to make a decision between a number of possible alternatives where only a single alternative is correct. Risky players respond faster but with more likelihood of failure. Cautious players wait longer for more evidence, increasing their likelihood of success, but at the expense of game time. By gathering empirical data for the player's response time and accuracy, we develop particle filter models. These models can then be used in real-time to categorise players into different ability and risk-taking levels.http://dx.doi.org/10.1155/2012/625476 |
| spellingShingle | Guy Hawkins Keith Nesbitt Scott Brown Dynamic Difficulty Balancing for Cautious Players and Risk Takers International Journal of Computer Games Technology |
| title | Dynamic Difficulty Balancing for Cautious Players and Risk Takers |
| title_full | Dynamic Difficulty Balancing for Cautious Players and Risk Takers |
| title_fullStr | Dynamic Difficulty Balancing for Cautious Players and Risk Takers |
| title_full_unstemmed | Dynamic Difficulty Balancing for Cautious Players and Risk Takers |
| title_short | Dynamic Difficulty Balancing for Cautious Players and Risk Takers |
| title_sort | dynamic difficulty balancing for cautious players and risk takers |
| url | http://dx.doi.org/10.1155/2012/625476 |
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