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Künstliche Intelligenz 28(4) - November 2014

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  • Zeitschriftenartikel
    Modeling Reality for Camera Registration in Augmented Reality Applications
    (KI - Künstliche Intelligenz: Vol. 28, No. 4, 2014) Pagani, Alain
  • Zeitschriftenartikel
    Empirical Evidence for Context-aware Interfaces to Pedestrian Navigation Systems
    (KI - Künstliche Intelligenz: Vol. 28, No. 4, 2014) Ludwig, Bernd; Müller, Manuel; Ohm, Christina
    For geographical mobile search tasks it is rarely sufficient to assist users identifying what location they are currently looking for, e.g. a store, cafe or museum. Often the user needs support in being guided to a retrieved location in a physical space. This means that mobile search is strongly connected with navigation. There is a large body of work indicating that navigating towards points of interest is challenging for many people. In this work we explore how to support best this part of the task by investigating how objects in the physical world—landmarks—can be used in information systems to guide people to their desired location. We present the results of a series of eye tracking studies on the orientation behavior of persons executing indoor navigation tasks. The main finding of the studies is that the contextual relevance and the function of a landmark for completing the task efficiently matters more than the context-free salience of the same landmark. The findings have implications for the design of mobile search systems that support geographical search tasks as they lead to new context-adaptive strategies for navigation systems to explain routes. We provide evidence that even the interface has to adapt its content on the state of the navigation task and the current spatial context in order to provide user- and context-adaptive intuitive interaction.
  • Zeitschriftenartikel
    The RACE Project
    (KI - Künstliche Intelligenz: Vol. 28, No. 4, 2014) Hertzberg, Joachim; Zhang, Jianwei; Zhang, Liwei; Rockel, Sebastian; Neumann, Bernd; Lehmann, Jos; Dubba, Krishna S. R.; Cohn, Anthony G.; Saffiotti, Alessandro; Pecora, Federico; Mansouri, Masoumeh; Konečný, Štefan; Günther, Martin; Stock, Sebastian; Lopes, Luis Seabra; Oliveira, Miguel; Lim, Gi Hyun; Kasaei, Hamidreza; Mokhtari, Vahid; Hotz, Lothar; Bohlken, Wilfried
    This paper reports on the aims, the approach, and the results of the European project RACE. The project aim was to enhance the behavior of an autonomous robot by having the robot learn from conceptualized experiences of previous performance, based on initial models of the domain and its own actions in it. This paper introduces the general system architecture; it then sketches some results in detail regarding hybrid reasoning and planning used in RACE, and instances of learning from the experiences of real robot task execution. Enhancement of robot competence is operationalized in terms of performance quality and description length of the robot instructions, and such enhancement is shown to result from the RACE system.
  • Zeitschriftenartikel
    Characterisation of Large Changes in Wind Power for the Day-Ahead Market Using a Fuzzy Logic Approach
    (KI - Künstliche Intelligenz: Vol. 28, No. 4, 2014) Martínez-Arellano, Giovanna; Nolle, Lars; Cant, Richard; Lotfi, Ahmad; Windmill, Christopher
    Wind power has become one of the renewable resources with a major growth in the electricity market. However, due to its inherent variability, forecasting techniques are necessary for the optimum scheduling of the electric grid, specially during ramp events. These large changes in wind power may not be captured by wind power point forecasts even with very high resolution numerical weather prediction models. In this paper, a fuzzy approach for wind power ramp characterisation is presented. The main benefit of this technique is that it avoids the binary definition of ramp event, allowing to identify changes in power output that can potentially turn into ramp events when the total percentage of change to be considered a ramp event is not met. To study the application of this technique, wind power forecasts were obtained and their corresponding error estimated using genetic programming and quantile regression forests. The error distributions were incorporated into the characterisation process, which according to the results, improve significantly the ramp capture. Results are presented using colour maps, which provide a useful way to interpret the characteristics of the ramp events.
  • Zeitschriftenartikel
    KI und Robotik für das Human Brain Project
    (KI - Künstliche Intelligenz: Vol. 28, No. 4, 2014) Visser, Ubbo
  • Zeitschriftenartikel
    Advanced Driver Assistance Systems and Animals
    (KI - Künstliche Intelligenz: Vol. 28, No. 4, 2014) Bendel, Oliver
    Advanced driver assistance systems are widely used. Some support and inform the driver. Others relieve him or her of certain tasks—and transform the human-guided system into a semi-autonomous one. For some years also fully autonomous systems have been on the roads, so-called self-driving cars, as prototypes of companies and within research projects. From the perspective of ethics—both of the special fields of ethics like animal ethics, information ethics and technology ethics and of machine ethics which can be understood as a counterpart to human ethics—advanced driver assistance systems raise various questions. The aim of this paper is to derive suggestions from animal ethics and other disciplines for the improvement and development of the systems. The basis are literature analysis and own classifications and considerations. The result is that there are many possibilities to expand existing systems and to develop new functions in the context with the aim to reduce the number of animal victims.
  • Zeitschriftenartikel
    Process-Optimized Planning for Cooperative Mobile Robots
    (KI - Künstliche Intelligenz: Vol. 28, No. 4, 2014) Scheuren, Stephan
  • Zeitschriftenartikel
    Technologies for the Fast Set-Up of Automated Assembly Processes
    (KI - Künstliche Intelligenz: Vol. 28, No. 4, 2014) Krüger, Norbert; Ude, Aleš; Petersen, Henrik Gordon; Nemec, Bojan; Ellekilde, Lars-Peter; Savarimuthu, Thiusius Rajeeth; Rytz, Jimmy Alison; Fischer, Kerstin; Buch, Anders Glent; Kraft, Dirk; Mustafa, Wail; Aksoy, Eren Erdal; Papon, Jeremie; Kramberger, Aljaž; Wörgötter, Florentin
    In this article, we describe technologies facilitating the set-up of automated assembly solutions which have been developed in the context of the IntellAct project (2011–2014). Tedious procedures are currently still required to establish such robot solutions. This hinders especially the automation of so called few-of-a-kind production. Therefore, most production of this kind is done manually and thus often performed in low-wage countries. In the IntellAct project, we have developed a set of methods which facilitate the set-up of a complex automatic assembly process, and here we present our work on tele-operation, dexterous grasping, pose estimation and learning of control strategies. The prototype developed in IntellAct is at a TRL4 (corresponding to ‘demonstration in lab environment’).
  • Zeitschriftenartikel
    Visual Learning of Semantic Concepts in Social Multimedia
    (KI - Künstliche Intelligenz: Vol. 28, No. 4, 2014) Borth, Damian
  • Zeitschriftenartikel
    Is Model-Based Robot Programming a Mirage? A Brief Survey of AI Reasoning in Robotics
    (KI - Künstliche Intelligenz: Vol. 28, No. 4, 2014) Pecora, Federico
    Researchers in AI and Robotics have in common the desire to “make robots intelligent”, evidence of which can be traced back to the earliest AI systems. One major contribution of AI to Robotics is the model-centered approach, whereby intelligence is the result of reasoning in models of the world which can be changed to suit different environments, physical capabilities, and tasks. Dually, robots have contributed to the formulation and resolution of challenging issues in AI, and are constantly eroding the modeling abstractions underlying AI problem solving techniques. Forty-eight years after the first AI-driven robot, this article provides an updated perspective on the successes and challenges which lie at the intersection of AI and Robotics.