Leveraging human-robot interaction in hospitality services: Incorporating the role of perceived value, empathy, and information sharing into visitors’ intentions to use social robots

Social robots have become pervasive in the tourism and hospitality service environments. The empirical understanding of the drivers of visitors' intentions to use robots in such services has become an urgent necessity for their sustainable deployment. Certainly, using social androids within hospitality services requires organisations' attentive commitment to value creation and fulfilling service quality expectations. In this paper, via structural equation modelling (SEM) and semi-structured interviews with managers, we conceptualise and empirically test visitors' intentions to use social robots in hospitality services. With data collected in Singapore's hospitality settings, we found visitors' intentions to use social robots stem from the effects of technology acceptance variables, service quality dimensions leading to perceived value, and two further dimensions from human robot interaction (HRI): empathy and information sharing. Analysis of these dimensions' importance provides a deeper understanding of novel opportunities managers may take advantage of to position social robot-delivered services in tourism and hospitality strategies

Um modelo de otimização para planejamento dinâmico de voo para grupos de drones por meio de sistema multiagente e leilões recursivos

Orientador: Eduardo TodtTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 03/07/2020Inclui referênciasÁrea de concentração: Ciência da ComputaçãoResumo: Este trabalho apresenta um modelo aplicado de cooperacao para otimizar voos de veiculos aereos nao tripulados do tipo quadricoptero, tambem conhecidos como Drones, com aplicacao na agricultura de precisao. O modelo utiliza Sistema Multiagente para permitir a abertura, que e a propriedade de inserir e retirar elementos do modelo a qualquer momento. Para garantir a dinamicidade, que e a caracteristica que o modelo tem de se recuperar de eventos adversos ou falhas, agentes cognitivos com BDI foram utilizados. Para garantir a troca de mensagens independente da quantidade de elementos no modelo, foi utilizado o protocolo FIPA Contract-NET. Um algoritmo distribuido de otimizacao utilizando leiloes recursivos tambem foi desenvolvido, o qual visa otimizar o tempo de voo, assim como o uso da bateria dos Drones, sendo a bateria a grande limitacao destes e inibindo sua utilizacao na agricultura de precisao. Esse algoritmo foi testado em seu modelo original e, posteriormente, refinado a partir de heuristicas e metodologias visando diminuir o numero de leiloes recursivos, assim como o tempo de processamento, em comparacao ao modelo original. Este modelo, apos aplicacao das heuristicas e metodologias, foi testado. Em cenarios contendo multiplos Drones, o desempenho foi 30% superior ao algoritmo dinamico encontrado na literatura que tambem pode ser aplicado em ambientes dinamicos. Do ponto de vista de abertura e dinamicidade, o modelo foi testado no simulador MultiDrone Simulator, permitindo gerar novos planos de voo, mesmo com eventos adversos. Os resultados dos testes em simulacao realizados sustentam que o modelo proposto apresenta comportamento como esperado, mostrando-se como uma plataforma promissora de pesquisa para uso de Drones em cenarios da agricultura de precisao, uma vez que este modelo permite a utilizacao de multiplos Drones em ambientes dinamicos e abertos, garantindo a otimizacao do tempo de voo, o que garante economia da bateria dos Drones. Palavras-chave: Drones, Sistema Multiagente, BDI, Leilao RecursivoAbstract: This work presents an applied model of cooperation to optimize flights of unmanned aerial vehicles like quadcopters, also known as Drones, involved in precision agriculture. This model uses a Multiagent System to allow up the opening, which is the property of inserting and removing elements from the model at any time. To allow dynamism, which is the characteristic that the model has to recover from adverse events or failures, cognitive agents with BDI structure were used. To guarantee the exchange of messages in dynamic number of elements, the FIPA Contract-NET protocol were used. A distributed optimization algorithm using recursive auctions was also developed, which aims to optimize the number of points covered by Drones. This model aims to optimize the flight time, which directly reflects the optimization of the Drone's battery use. This is a great limitation of this kind of aerial vehicle and which inhibits its use in precision agriculture. This algorithm was tested as original proposed and, later, refined from heuristics and methodologies in order to decrease the number of auctions, as well as the processing time. This model, after applying the heuristics and methodologies, was tested, and in scenarios containing multiple Drones, the performance was 30 % higher than the dynamic algorithm found in the literature that can also be applied in dynamic environments. From the point of view of openness and dynamics, the model was tested in the MultiDrone Simulator, allowing to generate new flight plans, even with the simulated adverse events. The results of the simulation tests carried out maintain that the proposed model behaves as expected, showing itself as a promising research platform for the use of drones in precision agriculture scenarios, since this model allows the use of multiple Drones in environments dynamic and open, guaranteeing the flight optimization, which ensures battery saving for Drones. Keywords: Drones, Multiagent System, BDI, Recursive Auction