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» Toward Scenario Adaptation for Learning
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IAT
2010
IEEE
13 years 6 months ago
Multiagent Meta-level Control for a Network of Weather Radars
It is crucial for embedded systems to adapt to the dynamics of open environments. This adaptation process becomes especially challenging in the context of multiagent systems. In t...
Shanjun Cheng, Anita Raja, Victor R. Lesser
ICRA
2009
IEEE
138views Robotics» more  ICRA 2009»
14 years 3 months ago
Which landmark is useful? Learning selection policies for navigation in unknown environments
Abstract— In general, a mobile robot that operates in unknown environments has to maintain a map and has to determine its own location given the map. This introduces significant...
Hauke Strasdat, Cyrill Stachniss, Wolfram Burgard
TSMC
1998
78views more  TSMC 1998»
13 years 8 months ago
Automata learning and intelligent tertiary searching for stochastic point location
—Consider the problem of a robot (learning mechanism or algorithm) attempting to locate a point on a line. The mechanism interacts with a random environment which essentially inf...
B. John Oommen, Govindachari Raghunath
CSCW
1994
ACM
14 years 16 days ago
From Implementation to Design: Tailoring and the Emergence of Systematization in CSCW
In this paper, we look at how people working in a governmental labor inspection agency tailor their shared PC environment. Starting with standard off-the-shelf software, the tailo...
Randall H. Trigg, Susanne Bødker
CORR
2010
Springer
147views Education» more  CORR 2010»
13 years 8 months ago
Learning Probabilistic Hierarchical Task Networks to Capture User Preferences
While much work on learning in planning focused on learning domain physics (i.e., action models), and search control knowledge, little attention has been paid towards learning use...
Nan Li, William Cushing, Subbarao Kambhampati, Sun...