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Auflistung Künstliche Intelligenz 28(1) - März 2014 nach Erscheinungsdatum
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- ZeitschriftenartikelTowards Learning of Generic Skills for Robotic Manipulation(KI - Künstliche Intelligenz: Vol. 28, No. 1, 2014) Metzen, Jan Hendrik; Fabisch, Alexander; Senger, Lisa; Gea Fernández, José; Kirchner, Elsa AndreaLearning versatile, reusable skills is one of the key prerequisites for autonomous robots. Imitation and reinforcement learning are among the most prominent approaches for learning basic robotic skills. However, the learned skills are often very specific and cannot be reused in different but related tasks. In the project 'Behaviors for Mobile Manipulation', we develop hierarchical and transfer learning methods which allow a robot to learn a repertoire of versatile skills that can be reused in different situations. The development of new methods is closely integrated with the analysis of complex human behavior.
- ZeitschriftenartikelCognitive Complexity and Analogies in Transfer Learning(KI - Künstliche Intelligenz: Vol. 28, No. 1, 2014) Ragni, Marco; Strube, GerhardThe ability to learn often requires transferring relational knowledge from one domain to another. It is difficult for humans and computers to identify the respective source domain from which relational characteristics can be applied to the target domain. An additional source of human reasoning difficulty is the complexity of the transformation function. In this article we investigate two domains in which the identification of relational patterns and of a transformation function are necessary: number series and geometrical analogy problems. Characteristics of the human processes are presented and existing cognitive models are discussed.
- ZeitschriftenartikelDoes AI Need a New Debate on Ethics?(KI - Künstliche Intelligenz: Vol. 28, No. 1, 2014) Schmid, Ute
- ZeitschriftenartikelTransfer for Automated Negotiation(KI - Künstliche Intelligenz: Vol. 28, No. 1, 2014) Chen, Siqi; Ammar, Haitham Bou; Tuyls, Karl; Weiss, GerhardLearning in automated negotiation is a difficult problem because the target function is hidden and the available experience for learning is rather limited. Transfer learning is a branch of machine learning research concerned with the reuse of previously acquired knowledge in new learning tasks, for example, in order to reduce the amount of learning experience required to attain a certain level of performance. This paper proposes a novel strategy based on a variation of TrAdaBoost—a classic instance transfer technique—that can be used in a multi-issue negotiation setting. The experimental results show that the proposed method is effective in a variety of application domains against the state-of-the-art negotiating agents.
- ZeitschriftenartikelRegularization-Based Multitask Learning With Applications to Genome Biology and Biological Imaging(KI - Künstliche Intelligenz: Vol. 28, No. 1, 2014) Widmer, Christian; Kloft, Marius; Lou, Xinghua; Rätsch, GunnarThe aim of multitask learning is to improve the generalization performance of a set of related tasks by exploiting complementary information about the tasks. In this paper, we review established approaches for regularization based multitask learning, sketch some recent developments, and demonstrate their applications in Computational Biology and Biological Imaging.
- ZeitschriftenartikelAutomated Transfer for Reinforcement Learning Tasks(KI - Künstliche Intelligenz: Vol. 28, No. 1, 2014) Bou Ammar, Haitham; Chen, Siqi; Tuyls, Karl; Weiss, GerhardReinforcement learning applications are hampered by the tabula rasa approach taken by existing techniques. Transfer for reinforcement learning tackles this problem by enabling the reuse of previously learned behaviours. To be fully autonomous a transfer agent has to: (1) automatically choose a relevant source task(s) for a given target, (2) learn about the relation between the tasks, and (3) effectively and efficiently transfer between tasks. Currently, most transfer frameworks require substantial human intervention in at least one of the previous three steps. This discussion paper aims at: (1) positioning various knowledge re-use algorithms as forms of transfer, and (2) arguing the validity and possibility of autonomous transfer by detailing potential solutions to the above three steps.
- ZeitschriftenartikelNews(KI - Künstliche Intelligenz: Vol. 28, No. 1, 2014)
- ZeitschriftenartikelAutomatic Generation of 3D Polygon Maps for Mobile Robots(KI - Künstliche Intelligenz: Vol. 28, No. 1, 2014) Wiemann, ThomasThe recent advance in 3D measurement technology, especially 3D laser scanners and RGB-D sensors like Microsoft Kinect, has made 3D point clouds feasibly accessible on mobile robots. Together with efficient SLAM algorithms, it is now possible to generate 3D point clouds of large environments like whole buildings or even cities at high speed and low cost. The problem is that these point clouds are usually not a suitable representation for classic robotic tasks like localization or even more sophisticated problems like scene interpretation. This thesis presents methods to create polygonal environment representations that can be used for semantic mapping and object recognition.
- ZeitschriftenartikelInterview with Peter Stone and Matthew E. Taylor(KI - Künstliche Intelligenz: Vol. 28, No. 1, 2014) Kudenko, Daniel
- ZeitschriftenartikelTransfer of Domain Knowledge in Plan Generation: Learning Goal-dependent Annulling Conditions for Actions(KI - Künstliche Intelligenz: Vol. 28, No. 1, 2014) Siebers, MichaelIn this paper we present an approach to avoid dead-ends during automated plan generation. A first-order logic formula can be learned that holds in a state if the application of a specific action will lead to a dead-end. Starting from small problems within a problem domain examples of states where the application of the action will lead to a dead-end will be collected. The states will be generalized using inductive logic programming to a first-order logic formula. We will show how different notions of goal-dependence could be integrated in this approach. The formula learned will be used to speed-up automated plan generation. Furthermore, it provides insight into the planning domain under consideration.