Abstract
Video games have a long history of use for educational and training purposes, as they provided increased motivation and learning for players. One of the limitations of using video games in this manner is, players still need to be tested outside of the game environment to test their learning outcomes. Traditionally, determining a player's skill level in a competitive game, requires players to compete directly with each other. Through the application of the Adaptive Training Framework, this work presents a novel method to determine the skill level of the player after each interaction with the video game. This is done by measuring the effort of a Dynamic Difficult Adjustment agent, without the need for direct competition between players. The experiments conducted in this research show that by measuring the players Heuristic Value Average, we can obtain the same ranking of players as state-of-the-art ranking systems, without the need for direct competition.
Original language | English |
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Title of host publication | Proceedings of the Australasian Computer Science Week Multiconference, ACSW 2018 |
Editors | Minh Ngoc Dinh |
Place of Publication | New York NY USA |
Publisher | Association for Computing Machinery (ACM) |
Number of pages | 7 |
ISBN (Electronic) | 9781450354363 |
DOIs | |
Publication status | Published - 2018 |
Externally published | Yes |
Event | Interactive Entertainment 2018 - Brisbane, Australia Duration: 29 Jan 2018 → 2 Feb 2018 |
Conference
Conference | Interactive Entertainment 2018 |
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Abbreviated title | IE 2018 |
Country/Territory | Australia |
City | Brisbane |
Period | 29/01/18 → 2/02/18 |
Keywords
- Dynamic Difficulty Adjustment
- Monte Carlo Tree Search
- Player Skill Ranking
- Video Games