Patterns for self-adaptive systems: agent-based simulations

Development of self-adaptive systems situated in open and uncertain environments is a great challenge in the community of software engineering due to the unpredictability of environment changes and the variety of self-adaptation manners. Explicit specification of expected changes and various self-adaptations at design-time, an approach often adopted by developers, seems ineffective. This paper presents an agent-based approach that combines two-layer self-adaptation mechanisms and reinforcement learning together to support the development and running of self-adaptive systems. The approach takes self-adaptive systems as multi-agent organizations and enables the agent itself to make decisions on self-adaptation by learning at run-time and at different levels. The proposed self-adaptation mechanisms that are based on organization metaphors enable self-adaptation at two layers: fine-grain behavior level and coarse-grain organization level. Corresponding reinforcement learning algorithms on self-adaptation are designed and integrated with the two-layer self-adaptation mechanisms. This paper further details developmental technologies, based on the above approach, in establishing self-adaptive systems, including extended software architecture for self-adaptation, an implementation framework, and a development process. A case study and experiment evaluations are conducted to illustrate the effectiveness of the proposed approach.

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ADAPT: An agent-based development toolkit and operation platform for self-adaptive systems

2017 IEEE Conference on Open Systems (ICOS) ◽

10.1109/icos.2017.8280274 ◽

2017 ◽

Cited By ~ 2

Author(s):

Yujie Li ◽

Qingshan Li ◽

Lu Wang ◽

Wen Cheng ◽

Tong Wu

Keyword(s):

Adaptive Systems ◽

Agent Based ◽

Operation Platform ◽

Self Adaptive

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Diffusion and persistence of false rumors in social media networks: implications of searchability on rumor self-correction on Twitter

Journal of Business Economics ◽

10.1007/s11573-020-01022-9 ◽

2021 ◽

Author(s):

Kathrin Eismann

Keyword(s):

Social Media ◽

Agent Based ◽

Social Media Networks ◽

Agent Based Simulations

AbstractSocial media networks (SMN) such as Facebook and Twitter are infamous for facilitating the spread of potentially false rumors. Although it has been argued that SMN enable their users to identify and challenge false rumors through collective efforts to make sense of unverified information—a process typically referred to as self-correction—evidence suggests that users frequently fail to distinguish among rumors before they have been resolved. How users evaluate the veracity of a rumor can depend on the appraisals of others who participate in a conversation. Affordances such as the searchability of SMN, which enables users to learn about a rumor through dedicated search and query features rather than relying on interactions with their relational connections, might therefore affect the veracity judgments at which they arrive. This paper uses agent-based simulations to illustrate that searchability can hinder actors seeking to evaluate the trustworthiness of a rumor’s source and hence impede self-correction. The findings indicate that exchanges between related users can increase the likelihood that trustworthy agents transmit rumor messages, which can promote the propagation of useful information and corrective posts.

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