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Auflistung Künstliche Intelligenz 31(2) - Mai 2017 nach Erscheinungsdatum
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- ZeitschriftenartikelErratum to: Kai-Florian Richter and Stephan Winter: Landmarks—GIScience for Intelligent Services(KI - Künstliche Intelligenz: Vol. 31, No. 2, 2017) Ishikawa, Toru
- ZeitschriftenartikelLandmarks for Location-Based Services (LBS) in Particular Navigation and Wayfinding(KI - Künstliche Intelligenz: Vol. 31, No. 2, 2017) Krisp, Jukka M.
- ZeitschriftenartikelTowards Efficiently Implementing Dodgson’s Formally Intractable Voting Rule(KI - Künstliche Intelligenz: Vol. 31, No. 2, 2017) Recknagel, Arne; Besold, Tarek R.Conflict of interest is the permanent companion of any population of agents (computational or biological). For that reason, the ability to compromise is of paramount importance, making voting a key element of societal mechanisms. A voting procedure often discussed in the literature and, due to its intuitiveness, also conceptually quite appealing is Charles Dodgson’s scoring rule, basically using the respective closeness to being a Condorcet winner for evaluating competing alternatives. In this paper, we offer insights into the practical limits of algorithms computing the exact Dodgson scores from a number of votes. While the problem itself is theoretically intractable, this work proposes and analyses five different solutions which try distinct approaches to practically solve the issue in an effective manner. Additionally, three of the discussed procedures can be run in parallel which has the potential of drastically improving computational performance on the problem.
- ZeitschriftenartikelOff-Screen Landmarks on Mobile Devices: Levels of Measurement and the Perception of Distance on Resized Icons(KI - Künstliche Intelligenz: Vol. 31, No. 2, 2017) Li, Rui; Zhao, JiayanWhile bringing portability and convenience to their users, the small screen size of mobile devices raises the concern that it might impact a user’s acquisition of spatial knowledge. Visualizing information of off-screen objects on mobile device has thus been introduced as a possible way to overcome this problem. Some approaches encode the distance to off-screen objects very well, but they have not considered the identities of objects, which could serve as easily-recognizable landmarks of recognition. Other approaches have addressed the visualization of distant objects’ identities as landmarks, but they have not considered the representation of distance to their actual locations. Following these approaches, this study introduces the use of visual variable size in the design of symbols for off-screen landmarks to translate both information about both direction and distance. To further investigate the efficiency of using these graduated size symbols, we apply ratio and ordinal levels of measurement to assign size to the symbols. Results show, size at the ordinal level leads to higher efficiency in understanding distance to off-screen locations. Both designs, however, yield challenges in participants’ understanding of distance based on the symbol’s size. As the initial step of investigating the use of visual variables in the design of symbols for off-screen landmarks, we suggest more visual variables be considered in follow-up designs to provide a more comprehensive understanding regarding the effectiveness of visualizing off-screen landmarks on mobile devices.
- ZeitschriftenartikelA Cognitive Observer-Based Landmark-Preference Model(KI - Künstliche Intelligenz: Vol. 31, No. 2, 2017) Röser, Florian
- ZeitschriftenartikelDissertation Abstract: Empirically Measuring Salience of Objects for Use in Pedestrian Navigation(KI - Künstliche Intelligenz: Vol. 31, No. 2, 2017) Kattenbeck, Markus
- ZeitschriftenartikelLandmark Extraction from Web-Harvested Place Descriptions(KI - Künstliche Intelligenz: Vol. 31, No. 2, 2017) Kim, Junchul; Vasardani, Maria; Winter, StephanLarge corpora of place descriptions provide abundant human spatial knowledge, different from the geometry-based information stored in current GIS. These place descriptions, used in everyday communication, frequently refer to landmarks. This paper suggests a model for extracting landmarks from web-harvested place descriptions, considering the landmark’s cognitive significance. The model allows landmarks to be extracted according to different contexts via web harvesting and text classification methods. In this work, an implementation of our approach is used to extract context-based landmarks for a target area—Melbourne in Australia.
- ZeitschriftenartikelIdentifying Landmark Candidates Beyond Toy Examples(KI - Künstliche Intelligenz: Vol. 31, No. 2, 2017) Richter, Kai-FlorianIncorporating references to landmarks in navigation systems requires having data on potential landmarks in the first place. While there have been many approaches in the scientific literature for identifying landmark candidates, these have hardly been picked up in actual, running systems. One major obstacle for this to happen may be that most—if not all—approaches presented so far are not scalable due to their underlying data requirements. In this paper, I will critically discuss existing approaches in light of their scalability. I will then suggest a way forward to more scalable solutions by combining in a smart way aspects of different approaches.
- ZeitschriftenartikelKai-Florian Richter and Stephan Winter: Landmarks—GIScience for Intelligent Services(KI - Künstliche Intelligenz: Vol. 31, No. 2, 2017) Ishikawa, Toru
- ZeitschriftenartikeliMRK: Demonstrator for Intelligent and Intuitive Human–Robot Collaboration in Industrial Manufacturing(KI - Künstliche Intelligenz: Vol. 31, No. 2, 2017) de Gea Fernández, José; Mronga, Dennis; Günther, Martin; Wirkus, Malte; Schröer, Martin; Stiene, Stefan; Kirchner, Elsa; Bargsten, Vinzenz; Bänziger, Timo; Teiwes, Johannes; Krüger, Thomas; Kirchner, FrankThis report describes an intelligent and intuitive dual-arm robotic system for industrial human–robot collaboration which provides the basis for further work between DFKI (Robotics Innovation Center) and Volkswagen Group (Smart Production Lab) in the field of intuitive and safe collaborative robotics in manufacturing scenarios. The final robot demonstrator developed in a pilot project possesses multiple sensor modalities for environment monitoring and is equipped with the ability for online collision-free dual-arm manipulation in a shared human–robot workspace. Moreover, the robot can be controlled via simple human gestures. The capabilities of the robotic system were validated at a mockup of a gearbox assembly station at a Volkswagen factory.