- Title
- Learning Movement Patterns for Improving the Skills of Beginner Level Players in Competitive MOBAs
- Creator
- Brown, Dane L, Bischof, Jonah
- Subject
- To be catalogued
- Date
- 2023
- Type
- text
- Type
- article
- Identifier
- http://hdl.handle.net/10962/464161
- Identifier
- vital:76482
- Identifier
- xlink:href="https://link.springer.com/chapter/10.1007/978-981-99-1624-5_45"
- Description
- League of Legends is a massively multiplayer online battle arena (MOBA)—a form of online competitive game in which teams of five players battle to demolish the opponent’s base. Expert players are aware of when to target, how to maximise their gold, and how to make choices. These are some of the talents that distinguish them from novices. The Riot API enables the retrieval of current League of Legends game data. This data is used to construct machine learning models that can benefit amateur players. Kills and goals can assist seasoned players understand how to take advantage of micro- and macro-teams. By understanding how professional players differ from novices, we may build tools to assist novices’ decision-making. 19 of 20 games for training a random forest (RF) and decision tree (DT) regressor produced encouraging results. An unseen game was utilised to evaluate the impartiality of the findings. RF and DT correctly predicted the locations of all game events in Experiment 1 with MSEs of 9.5 and 10.6. The purpose of the previous experiment was to fine-tune when novice players deviate from professional player behaviour and establish a solid commencement for battles. Based on this discrepancy, the system provided the player with reliable recommendations on which quadrant they should be in and which event/objective they should complete. This has shown to be a beneficial method for modelling player behaviour in future research.
- Format
- computer, online resource, application/pdf, 1 online resource (11 pages), pdf
- Publisher
- SpringerLink
- Language
- English
- Relation
- Inventive Systems and Control: Proceedings of ICISC 2023, Brown, D. and Bischof, J., 2023. Learning Movement Patterns for Improving the Skills of Beginner Level Players in Competitive MOBAs. In Inventive Systems and Control: Proceedings of ICISC 2023 (pp. 613-624). Singapore: Springer Nature Singapore, Inventive Systems and Control: Proceedings of ICISC 2023 p. 613 2023 2367-3389
- Rights
- Publisher
- Rights
- Use of this resource is governed by the terms and conditions of the SpringerLink Terms of Use Statement ( https://link.springer.com/termsandconditions)
- Rights
- Closed Access
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- Visitors: 62
- Downloads: 1
Collections
Brown, Dane A (Dr)
RU Department of Computer Science
SDG 09. Industry, Innovation and Infrastructure
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details | SOURCE1 | Learning Movement Patterns for Improving the Skills of Beginner Level Players in Competitive MOBAs.pdf | 708 KB | Adobe Acrobat PDF | View Details |