KI - Künstliche Intelligenz

Permanent URI for this communityhttps://dl.gi.de/handle/20.500.12116/11025

The Scientific journal "KI – Künstliche Intelligenz" is the official journal of the division for artificial intelligence within the "Gesellschaft für Informatik e.V." (GI) – the German Informatics Society – with contributions from throughout the field of artificial intelligence. The journal presents all relevant aspects of artificial intelligence – the fundamentals and tools, their use and adaptation for scientific purposes, and applications which are implemented using AI methods – and thus provides the reader with the latest developments in and well-founded background information on all relevant aspects of artificial intelligence. For all members of the AI community the journal provides quick access to current topics in the field and promotes vital interdisciplinary interchange.

Authors with most Documents  

Visser, Ubbo
21
Ragni, Marco
13
Richter, Kai-Florian
12
Sonntag, Daniel
12
Beetz, Michael
11

Browse

Author: search.filters.author.Beetz, Michael

Search Results

1 - 10 of 11
  • Journal Article
    Perception-Guided Mobile Manipulation Robots for Automation of Warehouse Logistics
    (Springer, 2019) Bartels, Georg; Beetz, Michael
  • Journal Article
  • Journal Article
    Episodic Memories for Safety-Aware Robots
    (Springer, 2019) Bartels, Georg; Beßler, Daniel; Beetz, Michael
    In the factories and distribution centers of the future, humans and robots shall work together in close proximity and even physically interact. This shift to joint human–robot teams raises the question of how to ensure worker safety. In this manuscript, we present a novel episodic memory system for safety-aware robots. Using this system, the robots can answer questions about their actions at the level of safety concepts. We built this system as an extension of the KnowRob framework and its notion of episodic memories. We evaluated the system in a safe physical human–robot interaction (pHRI) experiment, in which a robot had to sort surgical instruments while also ensuring the safety of its human co-workers. Our experimental results show the efficacy of the system to act as a robot’s belief state for online reasoning, as well as its ability to support offline safety analysis through its fast and flexible query interface. To this end, we demonstrate the system’s ability to reconstruct its geometric environment, course of action, and motion parameters from descriptions of safety-relevant events. We also show-case the system’s capability to conduct statistical analysis.
  • Journal Article
    Special Issue on Smart Production
    (Springer, 2019) Ruskowski, Martin; Legler, Tatjana; Beetz, Michael; Bartels, Georg
  • Journal Article
    Perception for Everyday Human Robot Interaction
    (Springer, 2016) Worch, Jan-Hendrik; Bálint-Benczédi, Ferenc; Beetz, Michael
    The ability to build robotic agents that can perform everyday tasks heavily depends on understanding how humans perform them. In order to achieve close to human understanding of a task and generate a formal representation of it, it is important to jointly reason about the human actions and the objects that are being acted on. We present a robotic perception framework for perceiving actions performed by a human in a household environment that can be used to answer questions such as “which object did the human act on?” or “which actions did the human perform?”. To do this we extend the RoboSherlock framework with the capabilities of detecting humans and objects at the same time, while simultaneously reasoning about the possible actions that are being performed.
  • Journal Article
    Open-EASE: A Cloud-Based Knowledge Service for Autonomous Learning
    (Springer, 2015) Tenorth, Moritz; Winkler, Jan; Beßler, Daniel; Beetz, Michael
    We present Open-EASE, a cloud-based knowledge base of robot experience data that can serve as episodic memory, providing a robot with comprehensive information for autonomously learning manipulation tasks. Open-EASE combines both robot and human activity data in a common, semantically annotated knowledge base, including robot poses, object information, environment models, the robot’s intentions and beliefs, as well as information about the actions that have been performed. A powerful query language and inference tools support reasoning about the data and retrieving information based on semantic queries. In this paper, we focus on applications of Open-EASE in the context of autonomous learning.
  • Journal Article
    Learning from Humans—Computational Models of Cognition-Enabled Control of Everyday Activity
    (Springer, 2010) Beetz, Michael; Buss, Martin; Radig, Bernd
    In recent years, we have seen tremendous advances in the mechatronic, sensing and computational infrastructure of robots, enabling them to act in several application domains faster, stronger and more accurately than humans do. Yet, when it comes to accomplishing manipulation tasks in everyday settings, robots often do not even reach the sophistication and performance of young children. In this article, we describe an interdisciplinary research approach in which we design computational models for controlling robots performing everyday manipulation tasks inspired by the observation of human activities.
  • Journal Article
    Interview with Eric Berger (Co-Director, Personal Robotics Program, Willow Garage)
    (Springer, 2010) Beetz, Michael; Kirsch, Alexandra
  • Journal Article
    CoTeSys—Cognition for Technical Systems
    (Springer, 2010) Buss, Martin; Beetz, Michael
    The CoTeSys cluster of excellence (Beetz et al. in Proceedings of the 30th German Conference on Artificial Intelligence, KI-2007, pp. 19–42, 2007) investigates cognition for technical systems such as robots and factories. Cognitive technical systems (CTS) are information processing systems equipped with artificial sensors and actuators, integrated and embedded into physical systems, and acting in a physical world. They differ from other technical systems as they perform cognitive control and have cognitive capabilities. Cognitive control orchestrates reflexive and habitual behavior in accord with longterm intentions. Cognitive capabilities such as perception, action, knowledge and models, reasoning, learning and planning turn technical systems into systems that “know what they are doing”. The cognitive capabilities result in systems of higher reliability, flexibility, adaptivity and better performance.
  • Journal Article
    Special Issue on Cognition for Technical Systems
    (Springer, 2010) Beetz, Michael; Kirsch, Alexandra