License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
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DOI: 10.4230/DagSemProc.10081.11
URN: urn:nbn:de:0030-drops-26379
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Ghallab, Malik

Modeling the Observed Behavior of a Robot through Machine Learning

10081.Ghallab.2637.pdf (0.07 MB)


Artificial systems are becoming more and more complex, almost as complex in some cases as natural systems. Up to now, the typical engineering question was "how do I design my system to behave according to some specifications". However, the incremental design process is leading to so complex artifacts that engineers are more and more addressing a quite different issue of "how
do I model the observed behavior of my system". Engineers are faced with the same problem as scientists studying natural phenomena. It may sound strange for an engineer to engage in observing and modeling what a system is doing, since this should be inferable from the models used in the system's design stage. However, a modular design of a complex artifact develops only local models that are combined on the basis of some composition principle of these models; it
seldom provides global behavior models.

BibTeX - Entry

  author =	{Ghallab, Malik},
  title =	{{Modeling the Observed Behavior of a Robot through Machine Learning}},
  booktitle =	{Cognitive Robotics},
  pages =	{1--1},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10081},
  editor =	{Gerhard Lakemeyer and Hector J. Levesque and Fiora Pirri},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-26379},
  doi =		{10.4230/DagSemProc.10081.11},
  annote =	{Keywords: Robotics, Machine Learning}

Keywords: Robotics, Machine Learning
Collection: 10081 - Cognitive Robotics
Issue Date: 2010
Date of publication: 27.10.2010

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