Publication
A review of dynamic difficulty adjustment methods for serious games
| dc.contributor.author | Lopes, JĂșlio Castro | |
| dc.contributor.author | Lopes, Rui Pedro | |
| dc.date.accessioned | 2014-10-28T12:22:36Z | |
| dc.date.available | 2014-10-28T12:22:36Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Rehabilitation games can be a novel and effective approach to mental and physical rehabilitation. Since patient's abilities differ, these types of games depend on a variety of levels of difficulty. Furthermore, it is prudent to make continuous adjustments to the difficulty in order to prevent overburdening the patient, whether on emotional or physical level. For this purpose Dynamic Difficulty Adjustment (DDA) can be a very interesting solution, as it allows for the dynamic adaptation of the game based on observed performance and on physiological data of the player. DDA has been used in games for many years as it allows tuning the game to match the player's ideal flow, keeping high levels of motivation and challenge. This paper reviews several DDA approaches used in rehabilitation and entertainment games. We concluded that many studies use Reinforcement Learning (RL) because it requires no pretraining and can adapt the game in real time without prior knowledge. | por |
| dc.identifier.citation | Lopes, JĂșlio Castro; Lopes, Rui Pedro (2022). A review of dynamic difficulty adjustment methods for serious games. In Optimization, Learning Algorithms and Applications, OL2A 2022. eISSN 1865-0937. 1754, p. 144-159 | por |
| dc.identifier.doi | 10.1007/978-3-031-23236-7_11 | |
| dc.identifier.eissn | 1865-0937 | |
| dc.identifier.issn | 1865-0929 | |
| dc.identifier.uri | http://hdl.handle.net/10198/11114 | |
| dc.language.iso | eng | por |
| dc.peerreviewed | yes | por |
| dc.publisher | Springer | |
| dc.subject | Dynamic difficulty adjustment | |
| dc.subject | Serious games | |
| dc.subject | Rehabilitation | |
| dc.subject | Reinforcement learning | |
| dc.title | A review of dynamic difficulty adjustment methods for serious games | por |
| dc.type | conference paper | |
| dspace.entity.type | Publication | |
| oaire.citation.title | Optimization, Learning Algorithms and Applications, OL2A 2022 | por |
| person.familyName | Lopes | |
| person.givenName | Rui Pedro | |
| person.identifier.ciencia-id | 8E14-54E4-4DB5 | |
| person.identifier.orcid | 0000-0002-9170-5078 | |
| rcaap.rights | openAccess | por |
| rcaap.type | conferenceObject | por |
| relation.isAuthorOfPublication | e1e64423-0ec8-46ee-be96-33205c7c98a9 | |
| relation.isAuthorOfPublication.latestForDiscovery | e1e64423-0ec8-46ee-be96-33205c7c98a9 |