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Lifelong Robot LearningJuly 1993
1993 Technical Report
Publisher:
  • University of Bonn
Published:01 July 1993
Reflects downloads up to 18 Feb 2025Bibliometrics
Abstract

No abstract available.

Cited By

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    Gkatzia D and Belvedere F "What's this?" Comparing Active learning Strategies for Concept Acquisition in HRI Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, (205-209)
  2. Koenig N and Matarić M (2018). Robot life-long task learning from human demonstrations, Autonomous Robots, 41:5, (1173-1188), Online publication date: 1-Jun-2017.
  3. ACM
    Fei G, Wang S and Liu B Learning Cumulatively to Become More Knowledgeable Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (1565-1574)
  4. Pentina A and Lampert C Lifelong learning with non-i.i.d. tasks Proceedings of the 29th International Conference on Neural Information Processing Systems - Volume 1, (1540-1548)
  5. Zhang D and Lu M (2011). Inconsistency-Induced Learning for Perpetual Learners, International Journal of Software Science and Computational Intelligence, 3:4, (33-51), Online publication date: 1-Oct-2011.
  6. Alpern S (2011). Find-and-Fetch Search on a Tree, Operations Research, 59:5, (1258-1268), Online publication date: 1-Sep-2011.
  7. Thomaz A and Breazeal C (2008). Teachable robots, Artificial Intelligence, 172:6-7, (716-737), Online publication date: 1-Apr-2008.
  8. ACM
    Banko M and Etzioni O Strategies for lifelong knowledge extraction from the web Proceedings of the 4th international conference on Knowledge capture, (95-102)
  9. Thomaz A and Breazeal C Reinforcement learning with human teachers Proceedings of the 21st national conference on Artificial intelligence - Volume 1, (1000-1005)
  10. Wyatt D, Philipose M and Choudhury T Unsupervised activity recognition using automatically mined common sense Proceedings of the 20th national conference on Artificial intelligence - Volume 1, (21-27)
  11. Revel A and Gaussier P Designing neural control architectures for an autonomous robot using vision to solve complex learning tasks Biologically inspired robot behavior engineering, (299-350)
  12. Azouaoui O and Chohra A (2019). Soft Computing Based Pattern Classifiers for the Obstacle Avoidance Behavior of Intelligent Autonomous Vehicles (IAV), Applied Intelligence, 16:3, (249-272), Online publication date: 27-Feb-2002.
  13. Chohra A, Farah A and Benmehrez C (1998). Neural Navigation Approach for Intelligent Autonomous Vehicles (IAV)in Partially Structured Environments, Applied Intelligence, 8:3, (219-233), Online publication date: 1-May-1998.
Contributors
  • Stanford University
  • Carnegie Mellon University
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