Mone: Mutation Oriented Norm Evolution

Wang, Xianchang; Fu, Ronghao; Zhang, Rui

doi:10.1109/access.2020.3037798

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…Inspired by Wang et al (2020c), we extend this study to a task-level setting and use an evolutionary algorithm for norm evolution. This minimizes the impact of anomalous results on the FERD system and improves its robustness.…”

Section: 2mentioning

confidence: 99%

“…However, the experts cannot predict all unexpected scenarios. The designed norms are not adequate for dealing with all scenarios and must evolve to adapt to changing scenarios (Zhao et al, 2017;Wang et al, 2020c). Sengupta and Yasser Mohammad (2021) studied a component that periodically replaces old strategies with new or better ones for self-enhancement.…”

Section: Introductionmentioning

confidence: 99%

“…This framework automatically learns during the offline stage and automatically evolves during the online stage. According to Wang et al (2020c), mutation is the primary strategy for norm evolution. Their method used the operator 'crossover' to evolve norms.…”

Section: Introductionmentioning

confidence: 99%

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ASTS: Autonomous switching of task-level strategies

Wang,

Lv,

Wang

et al. 2023

International Journal of Applied Mathematics and Computer Scien

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Autonomous coordination of multi-agent systems can improve the reaction and dispatching ability of multiple agents to emergency events. The existing research has mainly focused on the reactions or dispatching in specific scenarios. However, task-level coordination has not received significant attention. This study proposes a framework for autonomous switching of task-level strategies (ASTS), which can automatically switch strategies according to different scenarios in the task execution process. The framework is based on the blackboard system, which takes the form of an instance as an agent and the form of norm(s) as a strategy; it uses events to drive autonomous cooperation among multiple agents. A norm may be triggered when an event occurs. After the triggered norm is executed, it can change the data, state, and event in ASTS. To demonstrate the autonomy and switchability of the proposed framework, we develop a fire emergency reaction dispatch system. This system is applied to emergency scenarios involving fires. Five types of strategies and two control modes are designed for this system. Experiments show that this system can autonomously switch between different strategies and control modes in different scenarios with promising results. Our framework improves the adaptability and flexibility of multiple agents in an open environment and represents a solid step toward switching strategies at the task level.

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Section: 2mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ASTS: Autonomous switching of task-level strategies

Wang,

Lv,

Wang

et al. 2023

International Journal of Applied Mathematics and Computer Scien

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Crossover in mutation oriented norm evolution

Lv,

Wang,

Zhang

2024

Complex Intell. Syst.

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Norms are a coordination mechanism. They control agents’ behavior in a multiagent system (MAS) and need to evolve to cope with changing environments. Mutation oriented norm evolution is a strategies for allowing norms to evolve. However, this strategy simply adds some possible trigger condition constraints on the norms, which means that some agents are unable to perform actions. To address this problem, this paper presents a new strategy for norm evolution based on an improved crossover operator. First, this paper presents a power-set approach to improve the integrity of norm evolution. This approach can help ensure that all possible combinations of norms are considered during the analysis, providing a deeper understanding of how norms interact and evolve within a norm set. Then, to improve the efficiency of norm evolution, a trade-off between efficiency and completeness is proposed. This approach reduces the search space and improves efficiency, as not every power set combination needs to be searched; it also ensures completeness. Finally, the crossover operator in this strategy is improved based on the trade-off approach. Specifically, the triggers and expectations of one mutated norm enrich the triggers and expectations of other norms. All of these factors enrich the normative conditions through the trade-off approach. A MAS can take immediate action to adapt to new requirements or problems encountered, and quickly make normative changes and learn to respond appropriately to a new situation. The MAS is able to more clearly understand and learn about causality in the environment during norm evolution, and understand the connection between behavior and outcomes. The proposed strategy is applied to a case study of an unmanned vehicle system. The experimental results show that the trade-off approach has greater completeness and effectiveness in norm evolution. This strategy achieves a more complete and effective autonomous norm evolution. It helps the system achieve its goals better and reach better performance in terms of adaptability, helping it to function better in complex multiagent environments.

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Mone: Mutation Oriented Norm Evolution

Cited by 2 publications

References 13 publications

ASTS: Autonomous switching of task-level strategies

ASTS: Autonomous switching of task-level strategies

Crossover in mutation oriented norm evolution

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