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020 _a9783540492405
_9978-3-540-49240-5
024 7 _a10.1007/3-540-49240-2
_2doi
050 4 _aTJ212-225
050 4 _aTJ210.2-211.495
072 7 _aTJFM
_2bicssc
072 7 _aTEC007000
_2bisacsh
072 7 _aTJFM
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082 0 4 _a629.8
_223
245 1 0 _aLearning Robots
_h[electronic resource] :
_b6th European Workshop EWLR-6, Brighton, England, August 1-2, 1997 Proceedings /
_cedited by Andreas Birk, John Demiris.
250 _a1st ed. 1998.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c1998.
300 _aX, 194 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v1545
505 0 _aThe construction and acquisition of visual categories -- Q-Learning with Adaptive State Space Construction -- Modular Reinforcement Learning: An Application to a Real Robot Task -- Analysis and Design of Robot’s Behavior: Towards a Methodology -- Vision Based State Space Construction for Learning Mobile Robots in Multi Agent Environments -- Transmitting Communication Skills Through Imitation in Autonomous Robots -- Continual Robot Learning with Constructive Neural Networks -- Robot Learning and Self-Sufficiency: What the energy-level can tell us about a robot’s performance -- Perceptual grounding in robots -- A Learning Mobile Robot: Theory, Simulation and Practice -- Learning Complex Robot Behaviours by Evolutionary Computing with Task Decomposition -- Robot Learning using Gate-Level Evolvable Hardware.
520 _aRobot learning is a broad and interdisciplinary area. This holds with regard to the basic interests and the scienti c background of the researchers involved, as well as with regard to the techniques and approaches used. The interests that motivate the researchers in this eld range from fundamental research issues, such as how to constructively understand intelligence, to purely application o- ented work, such as the exploitation of learning techniques for industrial robotics. Given this broad scope of interests, it is not surprising that, although AI and robotics are usually the core of the robot learning eld, disciplines like cog- tive science, mathematics, social sciences, neuroscience, biology, and electrical engineering have also begun to play a role in it. In this way, its interdisciplinary character is more than a mere fashion, and leads to a productive exchange of ideas. One of the aims of EWLR-6 was to foster this exchange of ideas and to f- ther boost contacts between the di erent scienti c areas involved in learning robots. EWLR is, traditionally, a \European Workshop on Learning Robots". Nevertheless, the organizers of EWLR-6 decided to open up the workshop to non-European research as well, and included in the program committee we- known non-European researchers. This strategy proved to be successful since there was a strong participation in the workshop from researchers outside - rope, especially from Japan, which provided new ideas and lead to new contacts.
650 0 _aControl engineering.
650 0 _aRobotics.
650 0 _aAutomation.
650 0 _aArtificial intelligence.
650 0 _aComputer science
_xMathematics.
650 1 4 _aControl, Robotics, Automation.
650 2 4 _aArtificial Intelligence.
650 2 4 _aMathematical Applications in Computer Science.
700 1 _aBirk, Andreas.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aDemiris, John.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783540654803
776 0 8 _iPrinted edition:
_z9783662176344
830 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v1545
856 4 0 _uhttps://doi.org/10.1007/3-540-49240-2
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-LNC
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999 _c187735
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