.png){kind=icon}
.png){kind=icon}
Return
Multiobjective Competitive Co-Evolutionary Optimization and Regularity-Based Decision-Making for Two-Agent Wargame Strategy Optimization
.png){kind=icon}
OA
AI
DOI：10.1109/TEVC.2025.3565675.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Many practical problems involve multiple interdependent agents, each aiming to optimize its own objectives. Wargame strategy optimization, which requires optimizing strategies for at least two agents—attackers and defenders—presents unique challenges due to the interdependence of the agents’ strategies. This characteristic necessitates a co-evolutionary approach, where each agent’s strategy is continually adjusted in response to the other’s. The complexity increases when each agent pursues multiple conflicting objectives, resulting in Pareto-optimal strategy sets that require sequential decision-making (DM). To address these challenges, we introduce a novel multiobjective competitive co-evolutionary optimization (MoCCoEv) framework, specifically tailored for wargame strategy optimization. This framework integrates regularity-based search with an iterative and interactive DM approach, fostering a continuous interplay between co-evolving agents. Additionally, we introduce the concept of progressive shrinking, which interactively reduces the dimensions of the agents’ strategy parameters to mirror real-world DM by enforcing commitment to earlier moves and facilitating effective strategic choices. Our flexible and adaptable framework also supports alternative strategies, such as deception, and can be applied to other multiagent optimization problems.
Keywords:
Attacker-defender systems
co-evolution
decision-making (DM)
multiagent systems
multiobjective games
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
12
Papers:
1.8K
Citations:
2.4W