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  • 2020

  • Umbrico, Alessandro; Cesta, Amedeo; Cortellessa, Gabriella; Orlandini, Andrea (2020): A Holistic Approach to Behavior Adaptation for Socially Assistive Robots. In: International Journal of Social Robotics, S. 617-637. DOI: 10.1007/s12369-019-00617-9

    DOI: https://doi.org/10.1007/s12369-019-00617-9 

    Abstract: Socially assistive robotics aims at providing users with continuous support and personalized assistance, through appropriate social interactions. The design of robots capable of supporting people in heterogeneous tasks, raises several challenges among which the most relevant are the need to realise intelligent and continuous behaviours, robustness and flexibility of services and, furthermore, the ability to adapt to different contexts and needs. Artificial intelligence plays a key role in realizing cognitive capabilities like e.g., learning, context reasoning or planning that are highly needed in socially assistive robots. The integration of several of such capabilities is an open problem. This paper proposes a novel “cognitive approach” integrating ontology-based knowledge reasoning, automated planning and execution technologies. The core idea is to endow assistive robots with intelligent features in order to reason at different levels of abstraction, understand specific health-related needs and decide how to act in order to perform personalized assistive tasks. The paper presents such a cognitive approach pointing out the contribution of different knowledge contexts and perspectives, presents detailed functioning traces to show adaptation and personalization features, and finally discusses an experimental assessment proving the feasibility of the approach.

  • 2017

  • Quijano-Sanchez, Lara; Sauer, Christian; Recio-Garcia, Juan A.; Diaz-Agudo, Belen (2017): Make it personal. A social explanation system applied to group recommendations. In: Expert Systems with Applications 76, S. 36-48. DOI: 10.1016/j.eswa.2017.01.045

    Abstract: Recommender systems help users to identify which items from a variety of choices best match their needs and preferences. In this context, explanations act as complementary information that can help users to better comprehend the system’s output and to encourage goals such as trust, confidence in decision-making or utility. In this paper we propose a Personalized Social Individual Explanation approach (PSIE). Unlike other expert systems the PSIE proposal novelly includes explanations about the system’s group recommendation and explanations about the group’s social reality with the goal of inducing a positive reaction that leads to a better perception of the received group recommendations. Among other challenges, we uncover a special need to focus on “tactful” explanations when addressing users’ personal relationships within a group and to focus on personalized reassuring explanations that encourage users to accept the presented recommendations. Besides, the resulting intelligent system significatively increases users’ intent (likelihood) to follow the recommendations, users’ satisfaction and the system’s efficiency and trustworthiness. zitiert von 5

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