Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 26-35 |
| Seitenumfang | 10 |
| Fachzeitschrift | IEEE Transactions on Games |
| Jahrgang | 15 |
| Ausgabenummer | 1 |
| Publikationsstatus | Veröffentlicht - 2022 |
| Extern publiziert | Ja |
Abstract
Context Steering is a local approach to control an agent's movement in a dynamically changing scene. Recent works have formalized the context steering approach by Fray and presented a multi-objective view of the context steering problem. Combining a variety of different behaviors, which can be used multiple times in different configurations for different context maps, introduces a large number of parameters that need to be tuned to obtain well-performing agents. This work aims to use evolutionary algorithms to optimize context steering agents for various environments. A special focus lies on the evolution of agents that perform robustly across multiple variations of the same environment. To this end, we develop a real-valued encoding for a context steering agent along with three different fitness functions to represent different goals of the agent. Our experimental evaluation shows that an evolutionary optimization can produce agent configurations that perform well with respect to different tasks and show a high intra-task robustness. The proposed approach based on evolutionary optimization enables the user to optimize context steering agents such that they can explore environments while avoiding dynamic obstacles.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Informatik (insg.)
- Artificial intelligence
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
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in: IEEE Transactions on Games, Jahrgang 15, Nr. 1, 2022, S. 26-35.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Evolutionary Algorithm for Parameter Optimization of Context Steering Agents
AU - Dockhorn, Alexander
AU - Kirst, Martin
AU - Mostaghim, Sanaz
AU - Wieczorek, Martin
AU - Zille, Heiner
N1 - Publisher Copyright: © 2018 IEEE.
PY - 2022
Y1 - 2022
N2 - Context Steering is a local approach to control an agent's movement in a dynamically changing scene. Recent works have formalized the context steering approach by Fray and presented a multi-objective view of the context steering problem. Combining a variety of different behaviors, which can be used multiple times in different configurations for different context maps, introduces a large number of parameters that need to be tuned to obtain well-performing agents. This work aims to use evolutionary algorithms to optimize context steering agents for various environments. A special focus lies on the evolution of agents that perform robustly across multiple variations of the same environment. To this end, we develop a real-valued encoding for a context steering agent along with three different fitness functions to represent different goals of the agent. Our experimental evaluation shows that an evolutionary optimization can produce agent configurations that perform well with respect to different tasks and show a high intra-task robustness. The proposed approach based on evolutionary optimization enables the user to optimize context steering agents such that they can explore environments while avoiding dynamic obstacles.
AB - Context Steering is a local approach to control an agent's movement in a dynamically changing scene. Recent works have formalized the context steering approach by Fray and presented a multi-objective view of the context steering problem. Combining a variety of different behaviors, which can be used multiple times in different configurations for different context maps, introduces a large number of parameters that need to be tuned to obtain well-performing agents. This work aims to use evolutionary algorithms to optimize context steering agents for various environments. A special focus lies on the evolution of agents that perform robustly across multiple variations of the same environment. To this end, we develop a real-valued encoding for a context steering agent along with three different fitness functions to represent different goals of the agent. Our experimental evaluation shows that an evolutionary optimization can produce agent configurations that perform well with respect to different tasks and show a high intra-task robustness. The proposed approach based on evolutionary optimization enables the user to optimize context steering agents such that they can explore environments while avoiding dynamic obstacles.
KW - Autonomous Movement
KW - Context Steering
KW - Evolutionary Algorithms
KW - Games
KW - Multi-Criteria Optimization
KW - Optimization
KW - Robustness
KW - Search problems
KW - Steering systems
KW - Task analysis
KW - Tuning
KW - robustness
KW - context steering
KW - Autonomous movement
KW - multicriteria optimization
KW - evolut- ionary algorithms (EAs)
UR - http://www.scopus.com/inward/record.url?scp=85126286055&partnerID=8YFLogxK
U2 - 10.1109/TG.2022.3157247
DO - 10.1109/TG.2022.3157247
M3 - Article
AN - SCOPUS:85126286055
VL - 15
SP - 26
EP - 35
JO - IEEE Transactions on Games
JF - IEEE Transactions on Games
SN - 2475-1502
IS - 1
ER -