Alle Publikationen
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2020
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(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
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(2017): Online Recognition of Daily Activities by Color-Depth Sensing and Knowledge Models. In: SENSORS 17 (7)
Abstract: Visual activity recognition plays a fundamental role in several research fields as a way to extract semantic meaning of images and videos. Prior work has mostly focused on classification tasks, where a label is given for a video clip. However, real life scenarios require a method to browse a continuous video flow, automatically identify relevant temporal segments and classify them accordingly to target activities. This paper proposes a knowledge-driven event recognition framework to address this problem. The novelty of the method lies in the combination of a constraint-based ontology language for event modeling with robust algorithms to detect, track and re-identify people using color-depth sensing (Kinect® sensor). This combination enables to model and recognize longer and more complex events and to incorporate domain knowledge and 3D information into the same models. Moreover, the ontology-driven approach enables human understanding of system decisions and facilitates knowledge transfer across different scenes. The proposed framework is evaluated with real-world recordings of seniors carrying out unscripted, daily activities at hospital observation rooms and nursing homes. Results demonstrated that the proposed framework outperforms state-of-the-art methods in a variety of activities and datasets, and it is robust to variable and low-frame rate recordings. Further work will investigate how to extend the proposed framework with uncertainty management techniques to handle strong occlusion and ambiguous semantics, and how to exploit it to further support medicine on the timely diagnosis of cognitive disorders, such as Alzheimer’s disease.
Keywords: activities of daily living, activity recognition, assisted living, Bildsemantik, color-depth sensing, complex events, Ereigniserkennung, Ereignisverständnis, frame semantics, Informations- & Kommunikationstechnik, Knowledge representation, Kognitive Skills/Social Cognition, Mensch-Technik-Relationen (MTR), people detection and tracking, Realtechnik, senior monitoring, Sprachverstehen, Technik, Voraussetzungen für sozial angemessenes Verhalten, Weltwissen -
(2017): Reasoning about Imprecise Beliefs in Multi-Agent Systems with PDT Logic. In: KI - Künstliche Intelligenz 31 (1), S. 63-71. DOI: 10.1007/s13218-016-0455-7Keywords: abductive reasoning, Abduktion, abduktives Schließen, Agenten, Agenten-Überzeugung, Angemessenheit von Überzeugungen, Bedeutung, Belief updates, deutsche Community, Epistemische Logik/Modallogik/Doxastische Logik/Wissenslogik, Formalisierung, frame semantics, imprecise beliefs, Imprecise probabilities, Intellektualtechnik, Knowledge representation, Kogn. Architektur, kognitive Architekturen, Künstliche Intelligenz, Mensch-Technik-Relationen (MTR), model, Modell, Modellierung, multi-agent systems, Multi-Agenten-Systeme, PDT Logic, Probabilistic Doxastic Temporal Logic, Realtechnik, Technik, Ungenaue Überzeugungen, Wahrscheinlichkeit
2013
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(2013): Ontology-based state representations for intention recognition in human–robot collaborative environments. In: Robotics and Autonomous Systems 61 (11), S. 1224-1234. DOI: 10.1016/j.robot.2013.04.004
DOI: https://doi.org/10.1016/j.robot.2013.04.004 Abstract: In this paper, we describe a novel approach for representing state information for the purpose of intention recognition in cooperative human–robot environments. States are represented by a combination of spatial relationships in a Cartesian frame along with cardinal direction information. This approach is applied to a manufacturing kitting operation, where humans and robots are working together to develop kits. Based upon a set of predefined high-level state relationships that must be true for future actions to occur, a robot can use the detailed state information described in this paper to infer the probability of subsequent actions occurring. This would allow the robot to better help the human with the task or, at a minimum, better stay out of his or her way. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
Keywords: Bedienung & Handhabung, Environmental Effects, Human Machine Systems, human robot environments, Intention, intention recognition, Intentional Learning, Knowledge representation, Ontology (Philosophy), ontology based state representations, Recognition (Learning), Robotics, Spatial Learning, spatial relationships
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