Trust-based decision-making for smart and adaptive environments

Hammer, Stephan; Wiβner, Michael; André, Elisabeth

doi:10.1007/s11257-015-9160-8

Cited by 27 publications

(19 citation statements)

References 46 publications

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“…Additionally, prior attempt to define and model trustworthy properties has been context-specific and require generalization. A Bayesian network-based trust modeling used in [131] demonstrates where the trust impact of modifying system behavior is evaluated using a utility function. The model enforces system behaviors that yield the highest trust utility value depending on the status of context variables.…”

Section: B Trustworthiness and Trust Calibrationmentioning

confidence: 99%

“…The model enforces system behaviors that yield the highest trust utility value depending on the status of context variables. Despite the effort to computationally relate system properties to trust, the system under investigation [131] is not a learning system, and the system properties reflect user perceptions of them instead of actual measurements.…”

Section: B Trustworthiness and Trust Calibrationmentioning

confidence: 99%

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A Review on Human–Machine Trust Evaluation: Human-Centric and Machine-Centric Perspectives

Gebru

Zeleke

Blankson

et al. 2022

IEEE Trans. Human-Mach. Syst.

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As complex autonomous systems become increasingly ubiquitous, their deployment and integration into our daily lives will become a significant endeavor. Human-machine trust relationship is now acknowledged as one of the primary aspects that characterize a successful integration. In the context of human-machine interaction (HMI), proper use of machines and autonomous systems depends both on the human and machine counterparts. On one hand, it depends on how well the human relies on the machine regarding the situation or task at hand based on willingness and experience. On the other hand, it depends on how well the machine carries out the task and how well it conveys important information on how the job is done. Furthermore, proper calibration of trust for effective HMI requires the factors affecting trust to be properly accounted for and their relative importance to be rightly quantified. In this article, the functional understanding of humanmachine trust is viewed from two perspectives-human-centric and machine-centric. The human aspect of the discussion outlines factors, scales, and approaches, which are available to measure and calibrate human trust. The discussion on the machine aspect spans trustworthy artificial intelligence, built-in machine assurances, and ethical frameworks of trustworthy machines.

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Section: B Trustworthiness and Trust Calibrationmentioning

confidence: 99%

Section: B Trustworthiness and Trust Calibrationmentioning

confidence: 99%

A Review on Human–Machine Trust Evaluation: Human-Centric and Machine-Centric Perspectives

Gebru

Zeleke

Blankson

et al. 2022

IEEE Trans. Human-Mach. Syst.

View full text Add to dashboard Cite

show abstract

“…In this case, the authors use stereotypes to evaluate the profiles of new agents. On the other hand, the trust model proposed by [31] aims to evaluate whether an action would be perceived as trustworthy in a context where artificial systems interact with each other. A similar approach is proposed in [32], but in this case, the model is focused on human-robot interaction, where the robots predict the amount of trust that a potential partner (human) has about them.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Trust Model Based on Fuzzy Logic

Baqueta

Morveli-Espinoza

Lugo

et al. 2022

RITA

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In cooperative environments is common that agents delegate tasks to each other to achieve their goals since an agent may not have the capabilities or resources to achieve its objectives alone. However, to select good partners, the agent needs to deal with information about the abilities, experience, and goals of their partners. In this situation, the lack or inaccuracy of information may affect the agent's judgment about a given partner; and hence, increases the risk to rely on an untrustworthy agent. Therefore, in this work, we present a trust model that combines different pieces of information, such as social image, reputation, and references to produce more precise information about the characteristics and abilities of agents. An important aspect of our trust model is that it can be easily configured to deal with different evaluation criteria. For instance, as presented in our experiments, the agents are able to select their partners by availability instead of the expertise level. Besides, the model allows the agents to decide when their own opinions about a partner are more relevant than the opinions received from third parties, and vice-versa. Such flexibility can be explored in dynamic scenarios, where the environment and the behavior of the agents might change constantly.

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“…Trust, or rather the lack of trust by consumers in organizations charged with the implementation and management of smart grid technologies (e.g., electric utilities, governmental authorities), was reported as one of the key barrier values for smart grid acceptance (14 publications, [14,63,75,76,81,83,[90][91][92][94][95][96][97][98]).…”

Section: Moral Values That Form Barriers For Smart Grid Acceptancementioning

confidence: 99%

Moral Values as Factors for Social Acceptance of Smart Grid Technologies

et al. 2018

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Smart grid technologies are considered an important enabler in the transition to more sustainable energy systems because they support the integration of rising shares of volatile renewable energy sources into electricity networks. To implement them in a large scale, broad acceptance in societies is crucial. However, a growing body of research has revealed societal concerns with these technologies. To achieve sustainable energy systems, such concerns should be taken into account in the development of smart grid technologies. In this paper, we show that many concerns are related to moral values such as privacy, justice, or trust. We explore the effect of moral values on the acceptance of smart grid technologies. The results of our systematic literature review indicate that moral values can be both driving forces and barriers for smart grid acceptance. We propose that future research striving to understand the role of moral values as factors for social acceptance can benefit from an interdisciplinary approach bridging literature in ethics of technology with technology acceptance models.

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Trust-based decision-making for smart and adaptive environments

Cited by 27 publications

References 46 publications

A Review on Human–Machine Trust Evaluation: Human-Centric and Machine-Centric Perspectives

A Review on Human–Machine Trust Evaluation: Human-Centric and Machine-Centric Perspectives

An Adaptive Trust Model Based on Fuzzy Logic

Moral Values as Factors for Social Acceptance of Smart Grid Technologies

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