Output details
11 - Computer Science and Informatics
University of Aberdeen
Output title
Learning regions for building a world model from clusters in probability distributions
Type
E - Conference contribution
Name of conference/published proceedings
2011 IEEE International Conference on Development and Learning (ICDL 2011)
Volume number
-
Issue number
-
First page of article
n/a
ISSN of proceedings
-
Year of publication
2011
URL
-
Number of additional authors
1
Additional information
<24>This is a new (and more natural) approach to bootstrapping a domain model, without prior knowledge (an alternative to Mugan and Kuipers’s QLAP). It is very significant for developmental robotics because the learning method is applicable recursively, at multiple levels within a cognitive robot; e.g. from the sensor level right up to finding regions in higher order relationships which define concepts. This work has been extended to two-dimensional regions in rules (paper in ICDL-EpiRob 2013, currently no DOI). ICDL-EpiRob is the premier conference for developmental robotics, joining the previous EpiRob (since 2001) and ICDL (since 2002) conferences.
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Citation count
0
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
