Detection of privacy-sensitive situations for social robots in smart homes

Fernandes, Francisco Erivaldo; Yang, Guanci; Manh, Ha; Sheng, Weihua

doi:10.1109/coase.2016.7743474

Cited by 34 publications

(11 citation statements)

References 11 publications

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“…Finally, Wirtz et al [23] investigates the impact of service robots at three levels (Fig. 1): the micro level is concerned with the customer's experience of certain issues like privacy [44,45], security [46], dehumanization, depersonalization [19,23], and social deprivation [23]. It is also related to the kind of training that employees need to be able to deliver consistent services; the meso level focuses on the markets for a specific service and on market prices fluctuations due to the falling cost of using robots and their viability in the services industry's processes [28]; the macro level is related to social and employment issues [47], as well as to inequalities within and between societies [23].…”

Section: Service Robots and Ai Supporting Theoriesmentioning

confidence: 99%

Service robots in the hospitality industry: The case of Henn-na hotel, Japan

Reis

Melão

Salvadorinho

et al. 2020

Technology in Society

119

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Section: Service Robots and Ai Supporting Theoriesmentioning

confidence: 99%

Service robots in the hospitality industry: The case of Henn-na hotel, Japan

Reis

Melão

Salvadorinho

et al. 2020

Technology in Society

119

View full text Add to dashboard Cite

“…Additionally, this work is also part of our ongoing research that explores social robots in the smart home. We will investigate how to use GameEA to improve the accuracy of activity recognition and privacy detection [55].…”

Section: Discussionmentioning

confidence: 99%

Game Theory-Inspired Evolutionary Algorithm for Global Optimization

Yang

2017

Algorithms

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Many approaches that model specific intelligent behaviors perform excellently in solving complex optimization problems. Game theory is widely recognized as an important tool in many fields. This paper introduces a game theory-inspired evolutionary algorithm for global optimization (GameEA). A formulation to estimate payoff expectations is provided, which is a mechanism to make a player become a rational decision-maker. GameEA has one population (i.e., set of players) and generates new offspring only through an imitation operator and a belief-learning operator. An imitation operator adopts learning strategies and actions from other players to improve its competitiveness and applies these strategies to future games where one player updates its chromosome by strategically copying segments of gene sequences from a competitor. Belief learning refers to models in which a player adjusts his/her strategies, behavior or chromosomes by analyzing the current history information to improve solution quality. Experimental results on various classes of problems show that GameEA outperforms the other four algorithms on stability, robustness, and accuracy.

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“…Aside of avoiding cameras, for example if they are replaced by capacitive proximity sensors, researchers conducted studies with situation aware robots with cameras. Fernandes et al [Fernandes et al 2016] developed a system which detects privacy sensitive situations. Besides the system development, they conducted a survey with 449 participants.…”

Section: Related Workmentioning

confidence: 99%