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Статті в журналах з теми "Artificial empathy":
Asada, Minoru. "Towards Artificial Empathy." International Journal of Social Robotics 7, no. 1 (November 29, 2014): 19–33. http://dx.doi.org/10.1007/s12369-014-0253-z.
Asada, Minoru. "Development of artificial empathy." Neuroscience Research 90 (January 2015): 41–50. http://dx.doi.org/10.1016/j.neures.2014.12.002.
Stephan, Achim. "Empathy for Artificial Agents." International Journal of Social Robotics 7, no. 1 (November 18, 2014): 111–16. http://dx.doi.org/10.1007/s12369-014-0260-0.
Damiano, Luisa, Paul Dumouchel, and Hagen Lehmann. "Artificial Empathy: An Interdisciplinary Investigation." International Journal of Social Robotics 7, no. 1 (October 31, 2014): 3–5. http://dx.doi.org/10.1007/s12369-014-0259-6.
Wang, Yu, Yanzhong Zhang, Yanji Wang, Hao Zhang, Xinpeng Wang, Rongqing Xu, and Yi Tong. "Realization of Empathy Capability for the Evolution of Artificial Intelligence Using an MXene(Ti3C2)-Based Memristor." Electronics 13, no. 9 (April 24, 2024): 1632. http://dx.doi.org/10.3390/electronics13091632.
Montiel-Vázquez, Edwin Carlos, Jorge Adolfo Ramírez Uresti, and Octavio Loyola-González. "An Explainable Artificial Intelligence Approach for Detecting Empathy in Textual Communication." Applied Sciences 12, no. 19 (September 20, 2022): 9407. http://dx.doi.org/10.3390/app12199407.
Alsager Alzayed, Mohammad, Scarlett R. Miller, and Christopher McComb. "Empathic creativity: can trait empathy predict creative concept generation and selection?" Artificial Intelligence for Engineering Design, Analysis and Manufacturing 35, no. 4 (November 2021): 369–83. http://dx.doi.org/10.1017/s0890060421000196.
Yang, Hsuan-Chia, Annisa Ristya Rahmanti, Chih-Wei Huang, and Yu-Chuan Jack Li. "How Can Research on Artificial Empathy Be Enhanced by Applying Deepfakes?" Journal of Medical Internet Research 24, no. 3 (March 4, 2022): e29506. http://dx.doi.org/10.2196/29506.
Gómez-León, María Isabel. "Development of empathy through Socioemotional Artificial Intelligence." Papeles del Psicólogo - Psychologist Papers 43, no. 3 (2022): 218. http://dx.doi.org/10.23923/pap.psicol.2996.
Rostami, Mehdi, and Shokouh Navabinejad. "Artificial Empathy: User Experiences with Emotionally Intelligent Chatbots." AI and Tech in Behavioral and Social Sciences 1, no. 3 (2023): 19–27. http://dx.doi.org/10.61838/kman.aitech.1.3.4.
Дисертації з теми "Artificial empathy":
Anshar, Muh [Verfasser]. "Hardwiring Robot Empathy through Generation of Artificial Pain : Concetualizing Empathy into Adaptive Self-Awareness Framework for Robot / Muh Anshar." München : GRIN Verlag, 2019. http://d-nb.info/119552630X/34.
Gomes, Renata Correia Lima Ferreira. "Agentes verossímeis: uma investigação sobre a construção dos personagens autônomos nos videogames." Pontifícia Universidade Católica de São Paulo, 2008. https://tede2.pucsp.br/handle/handle/5142.
Conselho Nacional de Desenvolvimento Científico e Tecnológico
From the conceptual of character-oriented games and of simulation games the present research draws the idea of narrative games as an immersive simulation , to be experienced by the interactor through his or her traversing the virtual space of the game as an implicated character and through his or her interaction with the autonomous characters of the game. We carry through the hypothesis that the key for the implentation of a dramatic strutucture in the game albeit fundamentally different from that of film or theater is in the design of autonomous characters. These we take to be object inhabiting the space-time of the game, carrying a high degree of autonomy, complexity and intentionality, who, through the possibility of empathy, constitute in themselves a pathway towards a dramatic entity with or against whom the interactor has to act. To demonstrate that, we describe narrative as a evolutionary strategy towards a causal mindframe, which evolves side-be-side with the media that materialize it, from oral narratives to 3D interactive digital pieces. A second step toward demonstrating our hypothesis is to describe autonomous characters through Artificial Intelligence applied to narrative: the believable characters . We take as a reference the work of the research groups Oz and Synthetic Characters, and of Brazilian game designer Marcos Cuzziol. Finally, a third step is to approach the nature of the fiction character, through the work of Aristotle and Fernando Segolin, and the concept of empathy, under the approach of Evan Thompson. Empathy here is understood as pre-condition for the comprehension of the Other and of ourselves as affective, emotional and intentional entities. To illustrate this final view, we analyze a few aspects of the Creatures from the games Black & White I e II, in an attempt to point out how some qualities proposed are instantiated
A partir do universo dos games de personagem e games de simulação , a pesquisa em curso pretende descrever a narrativa nos games como uma simulação imersiva , a ser experimentada pelo interator através de seu percurso pelo espaço virtual do game no papel de um personagem e de sua interação com personagens autônomos, operados pelo software. Levamos adiante a hipótese de que a peça-chave para a implementação de uma estrutura dramática no game ainda que fundamentalmente diferente daquela conhecida no cinema e teatro - jaz no design dos personagens autônomos. Estes entendemos como objetos do espaço-tempo virtual, dotados de alto grau de autonomia, complexidade e intencionalidade, que, através da possibilidade de empatia, constituiriam em si o caminho para a emergência de uma vontade dramática com/contra a qual o interator teria que lidar. Para isto, descrevemos a narrativa como uma estratégia evolutiva de pensamento comunicacional e causal, que se desenvolve de acordo com os meios nos quais se materializa, da oralidade primária ao audiovisual digital interativo 3D. Um segundo passo trata de descrever os personagens autônomos, em sua faceta de Inteligência Artificial voltada para games e narrativas interativas: os agentes verossímeis . Para isso, tomamos como referência o trabalho dos grupo de pesquisa Oz e Sythetic Characters, assim como do game designer brasileiro Marcos Cuzziol. Um terceiro passo consiste em abordar a natureza da personagem de ficção a partir de Aristóteles, Fernando Segolin e do conceito de empatia, sobretudo na visão de Evan Thompson, esta entendida como pré-condição para a compreensão do outro e de nós mesmos como entidades afetivas, emocionais e intencionais. Para finalizar, procedemos a uma análise de alguns aspectos das Criaturas dos jogos Black & White I e II, na tentativa de descrever como se instanciam algumas qualidades das propostas anteriores
Wu, Di. "What Distinguishes Humans from Artificial Beings in Science Fiction World." Thesis, Blekinge Tekniska Högskola, Sektionen för planering och mediedesign, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2245.
Alexander, Ryan Cherian. "Artificial empathy : using vector space modeling and mixed scope alignment to infer emotional states of characters in stories." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106032.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 61-62).
Emotions greatly influence human cognition. Therefore, if we are to develop artificially intelligent programs that work closely with humans, we must ensure that they are capable of empathy. In an effort to realize the goal of emotionally aware programs, I created a multi-corpus informed vector space model to determine the emotions evoked by individual terms. I then combined that information with the semantic parse trees produced by the Genesis Story Understanding System to ascertain the emotions evoked by a single sentence. Additionally, I used the story aligner within Genesis to determine the emotions evoked by stories described over multiple sentences. My program can infer characters' emotional states based on their descriptions, the situations they are involved in, and the actions they perform. For instance, it infers that Alice is joyful from the sentence "Alice wins an award" and that James is probably experiencing sadness from the sentence "James is lonely." Additionally, the program can identify that Austin is likely surprised if "Austin has to take a test" and "Austin doesn't know about the test."
by Ryan Cherian Alexander.
M. Eng.
Basedow, Christina Anne [Verfasser], Arvid [Akademischer Betreuer] [Gutachter] Kappas, Ulrich [Gutachter] Kühnen, and Eric [Gutachter] Vanman. "Empathy with Robots? Exploring Emotional Responses to Artificial Entities / Christina Anne Basedow. Betreuer: Arvid Kappas. Gutachter: Arvid Kappas ; Ulrich Kühnen ; Eric Vanman." Bremen : IRC-Library, Information Resource Center der Jacobs University Bremen, 2016. http://d-nb.info/1104481081/34.
Colombel, Jessica. "Analyse du mouvement humain pour l'assistance à la personne : apport de la robustesse de l’observation et de l’analyse par contrôle optimal inverse." Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0210.
Biological motion has a lot of information, both physical and cognitive. Studies have shown that it is possible to determine a person's gender, emotion and even identity. These characteristics are accessible from information on the dynamics of the movement of polyarticulated bodies (e.g. the movement of the articulation points). Understanding and interpreting a person's behavior and state are abilities related to empathy. It is a faculty common to all mammals and is based on certain neural systems including, among others, mirror neurons. Given that empathy is an important part of social interactions in humans and more generally in animals, we can ask ourselves how our relationship with robots can be inspired by it.This leads us to the following problem: can robotic assistance to people use the interpretation of human movement, rich in physical and cognitive information, as a modality to improve the Human-Robot Interaction?To answer this question, we are working on observation tools and on a method of motion analysis that can be used in real time by a robotic system.Initially, we worked on the observation tools of human movement. Our objectives of robotic assistance in an ecological environment require the installation of sensors that affect the person's actions as little as possible. We have therefore chosen to study the Microsoft Kinect sensor which is an accessible depth sensor allowing to recover the Cartesian positions of the joints and extremities of the body. However, this type of sensor is subject to measurement noise that would prevent a fine analysis of the movement. We have therefore developed two methods to improve the measurement of this sensor based on the Extended Kalman Filter (EKF): an anthropometrically constrained EKF and a sensor fusion EKF. We have done the first study on the 2nd generation Kinect and the second on the 2nd and 3rd generations, allowing to highlight the differences between these two sensors.In a second time, we were interested in motion analysis methods and more specifically in the problem of Inverse Optimal Control (IOC). The objective of IOC is to identify the weights associated with a set of cost functions to be optimized to generate a given trajectory. In this thesis, we seek to analyze in real time human motion trajectories whose measurements, coming from sensors, are noisy. We have studied the reliability of the IOC resolution method called Approached, as a function of the measurement noise. We also provide an original approach to the IOC that poses a new view of the optimality of trajectories and allows us to introduce the concepts of Singularity Curves and Projection. We show in this paper tools to better understand and take into account the robustness issues of IOC
Widén, Tuva. "Artificial Resources : An Artistic Exploration of Material Subjectivity." Thesis, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:konstfack:diva-7294.
Henriques, Nuno Andrade da Cruz. "SensAI+Expanse : Prediction of Emotional Valence Changes on Humans in Context by an Artificial Agent Towards Empathy." Doctoral thesis, 2020. http://hdl.handle.net/10451/44892.
O campo da ciência cognitiva é suficientemente amplo no estudo interdisciplinar do cérebro, mente, e inteligência com uma comunidade de pesquisa científica em crescimento nas últimas décadas. Especificamente, juntando os dois campos da psicologia e inteligência artificial, é possível antever agentes, incorporados ou não, como humanos ou abstratos numa pulseira, com a capacidade de mudar significativamente a maneira como vivemos. A investigação descrita neste documento concebe o agente não antropomórfico com empatia adaptativa por sinergia da interação humano-artificial no sentido de uma melhor companhia entre agentes. Assim, os principais objetivos desta pesquisa são (a) construir um modelo de previsão adaptada a cada utilizador humano sobre mudanças de valência emocional em contexto; e (b) estudar comparativamente à idade, género, e comportamento humano a neutralidade e robustez do agente artificial na sua capacidade de previsão. O contexto inclui dados geograficamente localizados de sensores, análise de sentimento em texto, e de relatórios de valência emocional pelos humanos, estes eventos incluem informação temporal, usando um dispositivo móvel comum, tal como um telemóvel. Adicionalmente, para analisar e discutir os resultados de como alavancar esse modelo para se adaptarem estratégias de interação, de modo a promover aumento da empatia entre um agente não antropomórfico e o seu utilizador humano em contacto. Para cumprir estes objetivos é desenvolvido o SensAI+Expanse onde SensAI atua como um agente corporizado e de proximidade, e o Expanse abrange os recursos de aprendizagem por computador (machine learning) de forma eficiente. Isto é, uma plataforma distribuída, tolerante ao erro, móvel e baseada na nuvem (Cloud) informática, criada de raiz como ferramenta de pesquisa para reunir dados continuamente, ligada, e os processar para a aprendizagem automatizada por computador (automated machine learning) e a previsão. O estudo é desenvolvido com uma metodologia específica para evitar o viés das sociedades educadas, industrializadas, ricas e democráticas (conhecido como WEIRD). Este objetivo é alcançado através da recolha de dados em campo e alargado ao mundo inteiro (potencialmente) fazendo uso do repositório Google Play acessível ao público como local de publicação da aplicação SensAI. Os participantes elegíveis são diversos em idade, género, e no comportamento ao informarem a valência emocional. No sentido de equilibrar adequadamente a distribuição de género por idade, uma abordagem dicotómica utiliza a mediana das idades (M = 34). Em relação à duração da participação, dois terços (33/49) dos indivíduos elegíveis para análise permaneceram em interação pelo mínimo necessário de quatro semanas. A análise dos resultados mostra evidência de diferenças significativas de comportamento entre algumas combinações de idade e género em relação à valência emocional informada pelos utilizadores. Adicionalmente, os resultados de um estudo comparativo entre os melhores algoritmos atuais revelaram o Extreme Gradient Boosting, em média, o melhor modelo para previsão (F1 = 0; 91) com uso eficiente de energia e explicável usando inspeção de importância de cada caraterística (e.g., localização específica). Além disso, o agente artificial permaneceu neutro em relação à demografia humana e, simultaneamente, capaz de revelar idiossincrasias individuais. Portanto, as contribuições desta pesquisa incluem resultados com evidência, restritos à população e amostras de dados disponíveis, de diferenças de comportamento entre algumas combinações de intervalos etários e género. A principal contribuição é uma nova plataforma para estudos sobre as mudanças de valência emocional nos humanos e em contexto. Este sistema pode complementar e substituir (eventualmente) os tradicionais questionários com listas longas de questões para autoavaliação. A plataforma SensAI+Expanse contribui com várias partes, tais como (a) uma aplicação de dispositivo móvel (SensAI) com a capacidade de se adaptar e aprender de modo a prever estados de valência emocional com elevado desempenho; e (b) um serviço de computação em nuvem (Cloud), o SensAI Expanse, capaz de análise no momento e com módulos de processamento para a aprendizagem automatizada por computador (automated machine learning). Além disso, a abordagem de recolha de dados usando os sensores do telemóvel (smartphone sensing) adiciona uma contribuição no para análise da saúde ou do bem-estar em contínuo, não invasivo, e personalizado. Num futuro próximo, prevê-se um desenvolvimento interessante sobre as relações humano-agente em relação às interações afetivas. Adicionalmente, a medição das reações empáticas e a avaliação dos resultados das mesmas podem ser usados para verificar e validar o estado de saúde, e assim melhorar os cuidados e mudar significativamente a forma de viver dos seres humanos.
Anshar, Muh. "Evolving robot empathy through the generation of artificial pain in an adaptive self-awareness framework for human-robot collaborative tasks." Thesis, 2017. http://hdl.handle.net/10453/116217.
The application and use of robots in various areas of human life have been growing since the advent of robotics, and as a result, an increasing number of collaboration tasks are taking place. During a collaboration, humans and robots typically interact through a physical medium and it is likely that as more interactions occur, the possibility for humans to experience pain will increase. It is therefore of primary importance that robots should be capable of understanding the human concept of pain and to react to that understanding. However, studies reveal that the concept of human pain is strongly related to the complex structure of the human nervous system and the concept of Mind which includes concepts of Self-Awareness and Consciousness. Thus, developing an appropriate concept of pain for robots must incorporate the concepts of Self-Awareness and Consciousness. Our approach is firstly to acquire an appropriate concept of self-awareness as the basis for a robot framework. Secondly, it is to develop an internal capability for a framework for the internal state of the mechanism by inferring information captured through internal and external perceptions. Thirdly, to conceptualise an artificially created pain classification in the form of synthetic pain which mimics the human concept of pain. Fourthly, to demonstrate the implementation of synthetic pain activation on top of the robot framework, using a reasoning approach in relation to past, current and future predicted conditions. Lastly, our aim is to develop and demonstrate an empathy function as a counter action to the kinds of synthetic pain being generated. The framework allows robots to develop "self-consciousness" by focusing attention on two primary levels of self, namely subjective and objective. Once implemented, we report the results and provide insights from novel experiments designed to measure whether a robot is capable of shifting its "self-consciousness" using information obtained from exteroceptive and proprioceptive sensory perceptions. We consider whether the framework can support reasoning skills that allow the robot to predict and generate an accurate "pain" acknowledgement, and at the same time, develop appropriate counter responses. Our experiments are designed to evaluate synthetic pain classification, and the results show that the robot is aware of its internal state through the ability to predict its joint motion and produce appropriate artificial pain generation. The robot is also capable of alerting humans when a task will generate artificial pain, and if this fails, the robot can take considerable preventive actions through joint stiffness adjustment. In addition, an experiment scenario also includes the projection of another robot as an object of observation into an observer robot. The main condition to be met for this scenario is that the two robots must share a similar shoulder structure. The results suggest that the observer robot is capable of reacting to any detected synthetic pain occurring in the other robot, which is captured through visual perception. We find that integrating this awareness conceptualisation into a robot architecture will enhance the robot’s performance, and at the same time, develop a self-awareness capability which is highly advantageous in human-robot interaction. Building on this implementation and proof-of-concept work, future research will extend the pain acknowledgement and responses by integrating sensor data across more than one sensor using more sophisticated sensory mechanisms. In addition, the reasoning will be developed further by utilising and comparing the performance with different learning approaches and different collaboration tasks. The evaluation concept also needs to be extended to incorporate human-centred experiments. A major possible application of the proposal to be put forward in this thesis is in the area of assistive care robots, particularly robots which are used for the purpose of shoulder therapy.
Abath, Beatriz Maciel. "Empatia em agentes artificiais : proposta de um novo instrumento de avaliação." Master's thesis, 2021. http://hdl.handle.net/10451/51749.
Este trabalho visa contribuir para as discussões sobre o desenvolvimento de agentes artificiais empáticos, especialmente no que se refere à sua avaliação. Ainda não há instrumentos validados para medir a empatia em agentes artificiais e a maioria dos estudos nessa área utilizam medidas criadas ou adaptadas para cada pesquisa específica. Medidas validadas são essenciais para gerar dados confiáveis sobre aquilo que se pretende medir. O objetivo principal deste estudo é propor um instrumento de avaliação válido que possa vir a ser aplicado tanto em agentes artificiais quanto em seres humanos. O instrumento proposto avalia a empatia percebida por uma terceira pessoa, após observação da interação, e foi construído em Português, tendo sido baseado no instrumento de Davis, no instrumento de Toronto e em medidas não validadas aplicadas em estudos sobre agentes artificiais empáticos. A coleta de dados e a aplicação do instrumento foram realizadas pela internet, por meio da plataforma Qualtrics. As interações foram apresentadas em quatro vídeos legendados, 3 em que agentes artificiais (o chatbot Wysa, o personagem virtual Autotutor, e um robô do tipo NAO) interagem com seres humanos e 1 em que dois seres humanos interagem entre si. Os participantes foram convidados para participarem voluntariamente da pesquisa, via e-mail e redes sociais. O chatbot Wysa foi avaliado por 95 pessoas, o Autotutor por 96, o robô NAO por 100, e o ser humano por 99. Todos os participantes declararam ter 18 anos ou mais e serem fluentes na língua portuguesa, sendo 132 brasileiros, 50 portugueses e um argentino. Dentre os participantes, 103 declararam-se do sexo feminino e 73, do sexo masculino. A versão final do instrumento possui oito itens, que refletem componentes cognitivos, afetivos e comportamentais da empatia, e que se aproximam da definição de empatia proposta por Hoffman (1985). A análise fatorial apontou um fator único e os coeficientes do alfa de Cronbach situaram-se sempre acima de 0,8, indicando que o instrumento se mostrou válido e confiável. Com exceção do Autotutor, todos os agentes, incluindo o ser humano, foram bem avaliados como empáticos.
This research aims to contribute to the discussions on the development of empathetic artificial agents, especially regarding their evaluation. To date, there are no validated instruments to measure empathy in artificial agents and most studies in this area use measures created or adapted for each specific case. Validated measures are essential to generate reliable data on what it’s intended to measure. The main goal of this study is to propose a valid assessment instrument that can be applied to both artificial agents and human beings. The proposed instrument assesses the empathy perceived by a third person, after observing the interaction, and was built in Portuguese, having been based on the Interpersonal Reactivity Index from Davis, the Toronto Empathy Questionnaire and on non-validated measures applied in studies on empathic artificial agents. The instrument was applied through the Qualtrics platform, and all the data was collected via internet. The interactions were presented in four subtitled videos, 3 in which artificial agents (the chatbot Wysa, the virtual character Autotutor and a NAO robot) interact with human beings and 1 in which two human beings interact with each other. Participants were invited to participate voluntarily in the research, via email and social networks. The Wysa chatbot was evaluated by 95 people, the Autotutor by 96, the NAO robot by 100, and the human being by 99. All participants declared to be 18 years old or more and to be fluent in the Portuguese language, being 132 Brazilians, 50 Portuguese and one Argentine. Among the participants, 103 declared themselves to be female and 73, male. The final version of the instrument has eight items, which reflect cognitive, affective and behavioural components of empathy, and are consonant with the definition of empathy proposed by Hoffman (1985). The factor analysis pointed to a single factor and Cronbach's alpha coefficients were all above 0.8, indicating that the instrument proved to be valid and reliable. Except for the Autotutor, all agents, including the human being, were well evaluated as empathetic.
Книги з теми "Artificial empathy":
Lu, Shasha, Yinghui Zhou, Li Xiao, Min Ding, and Hye-Jin Kim. Audio and Visual Analytics in Marketing and Artificial Empathy. Now Publishers, 2022.
Rust, Roland T., and Ming-Hui Huang. Feeling Economy: How Artificial Intelligence Is Creating the Era of Empathy. Springer International Publishing AG, 2021.
McStay, Andrew. Emotional AI: The Rise of Empathic Media. SAGE Publications, Limited, 2017.
Evans, Dylan. Emotion: A Very Short Introduction. Oxford University Press, 2019. http://dx.doi.org/10.1093/actrade/9780198834403.001.0001.
McStay, Andrew. Emotional AI: The Rise of Empathic Media. SAGE Publications, Limited, 2018.
Maitra, Keya, and Jennifer McWeeny, eds. Feminist Philosophy of Mind. Oxford University PressNew York, 2022. http://dx.doi.org/10.1093/oso/9780190867614.001.0001.
Частини книг з теми "Artificial empathy":
Asada, Minoru. "Artificial Empathy." In Diversity in Harmony - Insights from Psychology, 19–41. Chichester, UK: John Wiley & Sons, Ltd, 2018. http://dx.doi.org/10.1002/9781119362081.ch2.
Masliković, Dejan, and Đurađ Grubišić. "Digital Empathy." In Applied Artificial Intelligence 2: Medicine, Biology, Chemistry, Financial, Games, Engineering, 190–93. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-60840-7_23.
Bryant, Peter T. "Cognitive Empathy." In Augmented Humanity, 139–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76445-6_5.
Kolonin, Anton. "Resource-Constrained Social Evidence Based Cognitive Model for Empathy-Driven Artificial Intelligence." In Artificial General Intelligence, 100–108. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97676-1_10.
Boukricha, Hana, Ipke Wachsmuth, Maria Nella Carminati, and Pia Knoeferle. "Empathy and Its Modulation in a Virtual Human." In KI 2013: Advances in Artificial Intelligence, 25–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40942-4_3.
Neroznikova, Yulia M., and Alexander V. Vartanov. "Reflection Mechanisms of Empathy Processes in Evoked Potentials." In Brain-Inspired Cognitive Architectures for Artificial Intelligence: BICA*AI 2020, 342–49. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65596-9_41.
Vargas Martin, Miguel, Eduardo Pérez Valle, and Sheri Horsburgh. "Artificial Empathy for Clinical Companion Robots with Privacy-By-Design." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 351–61. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70569-5_23.
Didenko, Valeriy D., Sergey G. Afanasiev, and Nikita A. Shcherbakov. "Secrets of the Empathy of Artificial Intelligence and Digital Economy. Philosophical and Cultural Aspects." In Studies in Systems, Decision and Control, 145–57. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56433-9_17.
Gierer, Alfred. "Evolution of Empathy as a Source of Human Altruism." In Prerational Intelligence: Adaptive Behavior and Intelligent Systems Without Symbols and Logic, Volume 1, Volume 2 Prerational Intelligence: Interdisciplinary Perspectives on the Behavior of Natural and Artificial Systems, Volume 3, 1036–45. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-010-0870-9_65.
Gama, Sandra, Gabriel Barata, Daniel Gonçalves, Rui Prada, and Ana Paiva. "SARA: Social Affective Relational Agent: A Study on the Role of Empathy in Artificial Social Agents." In Affective Computing and Intelligent Interaction, 507–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24600-5_54.
Тези доповідей конференцій з теми "Artificial empathy":
Sharma, Ashish, Inna W. Lin, Adam S. Miner, Dave C. Atkins, and Tim Althoff. "Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach (Extended Abstract)." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/747.
Yalcin, Ozge Nilay. "Evaluating Empathy in Artificial Agents." In 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, 2019. http://dx.doi.org/10.1109/acii.2019.8925498.
Hall, Theodore. "Artificial Gravity Visualization, Empathy, and Design." In Space 2006. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.2006-7321.
Zhu, Qihao, and Jianxi Luo. "Toward Artificial Empathy for Human-Centered Design: A Framework." In ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/detc2023-117266.
Matsumura, Tadayuki, Kanako Esaki, and Hiroyuki Mizuno. "Empathic Active Inference: Active Inference with Empathy Mechanism for Socially Behaved Artificial Agent." In The 2022 Conference on Artificial Life. Cambridge, MA: MIT Press, 2022. http://dx.doi.org/10.1162/isal_a_00496.
Siqueiros, Jesús M. "You, Robot: Empathy in a Hybrid World." In The 2021 Conference on Artificial Life. Cambridge, MA: MIT Press, 2021. http://dx.doi.org/10.1162/isal_a_00391.
Polajnar, Jernej, Behrooz Dalvandi, and Desanka Polajnar. "Does empathy between artificial agents improve agent teamwork?" In Cognitive Computing (ICCI-CC). IEEE, 2011. http://dx.doi.org/10.1109/coginf.2011.6016126.
Matsumura, Tadayuki, Kanako Esaki, Shunsuke Minusa, Yang Shao, Chihiro Yoshimura, and Hiroyuki Mizuno. "Social Emotional Valence for Regulating Empathy in Active Inference." In The 2023 Conference on Artificial Life. MIT Press, 2023. http://dx.doi.org/10.1162/isal_a_00573.
Priya, Priyanshu, Kshitij Mishra, Palak Totala, and Asif Ekbal. "PARTNER: A Persuasive Mental Health and Legal Counselling Dialogue System for Women and Children Crime Victims." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/686.
James, Jesin, Catherine Inez Watson, and Bruce MacDonald. "Artificial Empathy in Social Robots: An analysis of Emotions in Speech." In 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, 2018. http://dx.doi.org/10.1109/roman.2018.8525652.
Звіти організацій з теми "Artificial empathy":
Yatsymirska, Mariya. SOCIAL EXPRESSION IN MULTIMEDIA TEXTS. Ivan Franko National University of Lviv, February 2021. http://dx.doi.org/10.30970/vjo.2021.49.11072.