Details
| Original language | English |
|---|---|
| Title of host publication | 2021 IEEE Conference on Games, CoG 2021 |
| Publisher | IEEE Computer Society |
| ISBN (electronic) | 9781665438865 |
| Publication status | Published - 2021 |
| Externally published | Yes |
| Event | 2021 IEEE Conference on Games, CoG 2021 - Copenhagen, Denmark Duration: 17 Aug 2021 → 20 Aug 2021 |
Publication series
| Name | IEEE Conference on Computatonal Intelligence and Games, CIG |
|---|---|
| Volume | 2021-August |
| ISSN (Print) | 2325-4270 |
| ISSN (electronic) | 2325-4289 |
Abstract
This work concentrates on decision-making for autonomous movement of agents to simultaneously optimize several objectives which occur in their local environment. Such behavior can be achieved with steering algorithms, which have originally been designed for moving numerous agents simultaneously where occasional uncertainties are not noticeable by players. Nevertheless, concentrating on single individuals can reveal major flaws in their movement patterns such as oscillatory movement. For avoiding such problems, game makers are forced to develop higher-level abstractions for handling game-relevant special cases. Thus, eliminating the initial benefit of steering behaviors to be highly modular, lightweight, and controllable. This work enhances the context steering approach by Fray, which introduced discretized contextual information in the aggregation of a steering behavior's components. We combine this method with multi-criteria decision-making for controlling the agent's velocity direction and magnitude. The resulting approach is tested based on selected scenarios which show that the resulting approach is well suited to improve the agent's smooth and natural movement. Based on our observations we propose suitable parameterizations of the designed method and discuss advantages and disadvantages of made enhancements.
Keywords
- AI, Autonomous Movement, Context Steering, Multi-Criteria Optimization, NPC
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Software
Cite this
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2021 IEEE Conference on Games, CoG 2021. IEEE Computer Society, 2021. (IEEE Conference on Computatonal Intelligence and Games, CIG; Vol. 2021-August).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Multi-Objective Optimization and Decision-Making in Context Steering
AU - Dockhorn, Alexander
AU - Mostaghim, Sanaz
AU - Kirst, Martin
AU - Zettwitz, Martin
PY - 2021
Y1 - 2021
N2 - This work concentrates on decision-making for autonomous movement of agents to simultaneously optimize several objectives which occur in their local environment. Such behavior can be achieved with steering algorithms, which have originally been designed for moving numerous agents simultaneously where occasional uncertainties are not noticeable by players. Nevertheless, concentrating on single individuals can reveal major flaws in their movement patterns such as oscillatory movement. For avoiding such problems, game makers are forced to develop higher-level abstractions for handling game-relevant special cases. Thus, eliminating the initial benefit of steering behaviors to be highly modular, lightweight, and controllable. This work enhances the context steering approach by Fray, which introduced discretized contextual information in the aggregation of a steering behavior's components. We combine this method with multi-criteria decision-making for controlling the agent's velocity direction and magnitude. The resulting approach is tested based on selected scenarios which show that the resulting approach is well suited to improve the agent's smooth and natural movement. Based on our observations we propose suitable parameterizations of the designed method and discuss advantages and disadvantages of made enhancements.
AB - This work concentrates on decision-making for autonomous movement of agents to simultaneously optimize several objectives which occur in their local environment. Such behavior can be achieved with steering algorithms, which have originally been designed for moving numerous agents simultaneously where occasional uncertainties are not noticeable by players. Nevertheless, concentrating on single individuals can reveal major flaws in their movement patterns such as oscillatory movement. For avoiding such problems, game makers are forced to develop higher-level abstractions for handling game-relevant special cases. Thus, eliminating the initial benefit of steering behaviors to be highly modular, lightweight, and controllable. This work enhances the context steering approach by Fray, which introduced discretized contextual information in the aggregation of a steering behavior's components. We combine this method with multi-criteria decision-making for controlling the agent's velocity direction and magnitude. The resulting approach is tested based on selected scenarios which show that the resulting approach is well suited to improve the agent's smooth and natural movement. Based on our observations we propose suitable parameterizations of the designed method and discuss advantages and disadvantages of made enhancements.
KW - AI
KW - Autonomous Movement
KW - Context Steering
KW - Multi-Criteria Optimization
KW - NPC
UR - http://www.scopus.com/inward/record.url?scp=85122939895&partnerID=8YFLogxK
U2 - 10.1109/CoG52621.2021.9619155
DO - 10.1109/CoG52621.2021.9619155
M3 - Conference contribution
AN - SCOPUS:85122939895
T3 - IEEE Conference on Computatonal Intelligence and Games, CIG
BT - 2021 IEEE Conference on Games, CoG 2021
PB - IEEE Computer Society
T2 - 2021 IEEE Conference on Games, CoG 2021
Y2 - 17 August 2021 through 20 August 2021
ER -