Agents united:An open platform for multi-agent conversational systems

The development of applications with intelligent virtual agents (IVA) often comes with integration of multiple complex components. In this article we present the Agents United Platform: an open source platform that researchers and developers can use as a starting point to setup their own multi-IVA applications. The new platform provides developers with a set of integrated components in a sense-remember-think-act architecture. Integrated components are a sensor framework, memory component, Topic Selection Engine, interaction manager (Flipper), two dialogue execution engines, and two behaviour realisers (ASAP and GRETA) of which the agents can seamlessly interact with each other. This article discusses the platform and its individual components. It also highlights some of the novelties that arise from the integration of components and elaborates on directions for future work

Virtual Patient for Training Doctors to Break Bad News

International audienceThe way doctors deliver bad news has a significant impact on the therapeutic process: disease evolution, adherence with treatment recommendations, litigation possibilities (Andrade et al., 2010). However, both experienced clinicians and medical trainees consider this task as difficult, daunting, and stressful. Nowadays, training health care professional to break bad news, recommended by the French Haute Autorité de la Santé (HAS), is organized as workshops during which doctors disclose bad news to actors playing the role of patient. In our project, we are developing an embodied conversational agent simulating a patient to train doctors to break bad news. The embodied conversational agent is incorporated in an immersive virtual reality environment (a CAVE) integrating several sensors to detect and recognize in real time the verbal and non-verbal behavior of the doctors interacting with the virtual patient. The virtual patient will adapt its behavior depending on the doctor's verbal and non-verbal behavior. The methodology used to construct the virtual patient behavior model is based on a quantitative and qualitative analysis of corpus of doctors training sessions

Virtual Patient for Training Doctors to Break Bad News

International audienceThe way doctors deliver bad news has a significant impact on the therapeutic process: disease evolution, adherence with treatment recommendations, litigation possibilities (Andrade et al., 2010). However, both experienced clinicians and medical trainees consider this task as difficult, daunting, and stressful. Nowadays, training health care professional to break bad news, recommended by the French Haute Autorité de la Santé (HAS), is organized as workshops during which doctors disclose bad news to actors playing the role of patient. In our project, we are developing an embodied conversational agent simulating a patient to train doctors to break bad news. The embodied conversational agent is incorporated in an immersive virtual reality environment (a CAVE) integrating several sensors to detect and recognize in real time the verbal and non-verbal behavior of the doctors interacting with the virtual patient. The virtual patient will adapt its behavior depending on the doctor's verbal and non-verbal behavior. The methodology used to construct the virtual patient behavior model is based on a quantitative and qualitative analysis of corpus of doctors training sessions

Agents United: An Open Platform for Multi-Agent Conversational Systems

The authors would like to thank all students and researchers who have worked with various versions of the platform and who have provided valuable feedback for its development. This work was supported by the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 769553 (Council of Coaches). The Agents United Alliance is supported by the Personalised eHealth Technology programme of the University of Twente.The development of applications with intelligent virtual agents (IVA) often comes with integration of multiple complex components. In this article we present the Agents United Platform: an open source platform that researchers and developers can use as a starting point to setup their own multi-IVA applications. The new platform provides developers with a set of integrated components in a sense-remember-think-act architecture. Integrated components are a sensor framework, memory component, Topic Selection Engine, interaction manager (Flipper), two dialogue execution engines, and two behaviour realisers (ASAP and GRETA) of which the agents can seamlessly interact with each other. This article discusses the platform and its individual components. It also highlights some of the novelties that arise from the integration of components and elaborates on directions for future work.European Union's Horizon 2020 research and innovation programme 769553Personalised eHealth Technology programme of the University of Twent

Data-driven model of virtual patient for doctor social training

International audienceno abstrac