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  • 2018

  • Shank, Daniel B.; DeSanti, Alyssa (2018): Attributions of Morality and Mind to Artificial Intelligence after Real-World Moral Violations. In: Computers in Human Behavior, S. 401-411. DOI: 10.1016/j.chb.2018.05.014

    Abstract: Highlights: •AI’s real-world moral violations only reported 43.5% of the time •Knowledge of algorithm increases moral violation perception •AI not organization, programmer, or use is attributed wrongness •AI attributed mind, awareness, intentionality, justification, and responsibility

    Keywords: algorithm, Artificial intelligence, attributions, Künstliche Intelligenz, Mensch-Technik-Relation, Moral, Morality, perceived mind, responsibility, science direct, Soziales Kompetenztraining

  • 2005

  • Sujan, Vivek A.; Meggiolaro, Marco A. (2005): Intelligent and Efficient Strategy for Unstructured Environment Sensing Using Mobile Robot Agents. In: Journal of Intelligent & Robotic Systems 43 (2-4), S. 217-253. DOI: 10.1007/s10846-004-3063-y

    DOI: https://doi.org/10.1007/s10846-004-3063-y 

    Abstract: In field environments it is not usually possible to provide robots in advance with valid geometric models of its task and environment. The robot or robot teams need to create these models by scanning the environment with its sensors. Here, an information-based iterative algorithm to plan the robot’s visual exploration strategy is proposed to enable it to most efficiently build 3D models of its environment and task. The method assumes mobile robot (or vehicle) with vision sensors mounted at a manipulator end-effector (eye-in-hand system). This algorithm efficiently repositions the systems’ sensing agents using an information theoretic approach and fuses sensory information using physical models to yield a geometrically consistent environment map. This is achieved by utilizing a metric derived from Shannon’s information theory to determine optimal sensing poses for the agent(s) mapping a highly unstructured environment, This map is then distributed among the agents using an information-based relevant data reduction scheme, This method is particularly well suited to unstructured environments, where sensor uncertainty is significant. Issues addressed include model-based multiple sensor data fusion, and uncertainty and vehicle suspension motion compensation. Simulation results show the effectiveness of this algorithm. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

    Keywords: algorithm, Algorithms, Bedienung & Handhabung, Environment, geometric models, Geometry, Information theory, mobile robot agents, MODELS, Robotics, Strategies, unstructured environment, visual exploration strategy
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