Complementing Agents with Cognitive Services: A Case Study in Healthcare

Montagna, Sara; Mariani, Stefano; Gamberini, Emiliano; Ricci, Alessandro; Zambonelli, Franco

doi:10.1007/s10916-020-01621-7

Cited by 10 publications

(2 citation statements)

References 33 publications

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“…In this way, the user's fine-grained requirements such as ordering restaurants and checking the weather cannot be dealt with in task-oriented dialogues. In cognitive service, when a user puts forward a requirement, the bot needs to inform the user of the service resources (knowledge) which satisfy the requirement [35]. Therefore, user's requirement (dialogue goal), service resources should be unified as external information as the input of the generative model.…”

Section: Response Generationmentioning

confidence: 99%

Requirements Elicitation in Cognitive Service for Recommendation

Zhang¹,

Tu²,

Xu³

et al. 2022

Preprint

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Nowadays, cognitive service provides more interactive way to understand users' requirements via human-machine conversation. In other words, it has to capture users' requirements from their utterance and respond them with the relevant and suitable service resources. To this end, two phases must be applied: I.Sequence planning and Real-time detection of user requirement, II.Service resource selection and Response generation. The existing works ignore the potential connection between these two phases. To model their connection, Two-Phase Requirement Elicitation Method is proposed. For the phase I, this paper proposes a user requirement elicitation framework (URef) to plan a potential requirement sequence grounded on user profile and personal knowledge base before the conversation. In addition, it can also predict user's true requirement and judge whether the requirement is completed based on the user's utterance during the conversation. For the phase II, this paper proposes a response generation model based on attention, SaRSNet. It can select the appropriate resource (i.e. knowledge triple) in line with the requirement predicted by URef, and then generates a suitable response for recommendation. The experimental results on the open dataset DuRecDial have been significantly improved compared to the baseline, which proves the effectiveness of the proposed methods.

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Section: Response Generationmentioning

confidence: 99%

Requirements Elicitation in Cognitive Service for Recommendation

Zhang¹,

Tu²,

Xu³

et al. 2022

Preprint

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“…Pervasive hypermedia environments provide DCs with uniform access not only to their local deployment context (e.g., sensor readings), but also to global knowledge and services. Proactive DCs then have the ability to make decisions based on their own perception and knowledge about the local environment (e.g., what people are experiencing in that context at that time) as well as cognitive services discovered through the Web [37]. This raises the need for agent architectures that provide a proper level of abstraction to (i) support working in (world-wide) pervasive hypermedia environments, and (ii) balance local and global access to knowledge in real-time.…”

Section: Hypermedia Masmentioning

confidence: 99%

Proactive Digital Companions in Pervasive Hypermedia Environments

García

Mayer

Ricci

et al. 2020

2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC)

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Artificial companions and digital assistants have been investigated for several decades, from research in the autonomous agents and social robots areas to the highly popular voice-enabled digital assistants that are already in widespread use (e.g., Siri and Alexa). Although these companions provide valuable information and services to people, they remain reactive entities that operate in isolated environments waiting to be asked for help. The Web is now emerging as a uniform hypermedia fabric that interconnects everything (e.g., devices, physical objects, abstract concepts, digital services), thereby enabling unprecedented levels of automation and comfort in our professional and private lives. However, this also results in increasingly complex environments that are becoming unintelligible to everyday users. To ameliorate this situation, we envision proactive Digital Companions that take advantage of this new generation of pervasive hypermedia environments to provide assistance and protection to people. In addition to Digital Companions perceiving a person's environment through vision and sound, pervasive hypermedia environments provide them with means to further contextualize the situation by exploiting information from available connected devices, and give them access to rich knowledge bases that allow to derive relevant actions and recommendations.

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