87 research outputs found
Bridging the lesson distribution gap
Paper presented at The 17th International Joint Conference on Artificial Intelligence, IJCAI 2001, Seattle, WA: pp. 987-992.Many organizations employ lessons learned (LL)
processes to collect, analyze, store, and distribute,
validated experiential knowledge (lessons) of their
members that, when reused, can substantially improve
organizational decision processes. Unfortunately,
deployed LL systems do not facilitate lesson reuse and
fail to bring lessons to the attention of the users when
and where they are needed and applicable (i.e., they
fail to bridge the lesson distribution gap). Our
approach for solving this problem, named monitored
distribution, tightly integrates lesson distribution with
these decision processes. We describe a case-based
implementation of monitored distribution (ALDS) in a
plan authoring tool suite (HICAP). We evaluate its
utility in a simulated military planning domain. Our
results show that monitored distribution can
significantly improve plan evaluation measures for this
domain
Bridging the lesson distribution gap
Paper presented at The 17th International Joint Conference on Artificial Intelligence, IJCAI 2001, Seattle, WA: pp. 987-992.Many organizations employ lessons learned (LL)
processes to collect, analyze, store, and distribute,
validated experiential knowledge (lessons) of their
members that, when reused, can substantially improve
organizational decision processes. Unfortunately,
deployed LL systems do not facilitate lesson reuse and
fail to bring lessons to the attention of the users when
and where they are needed and applicable (i.e., they
fail to bridge the lesson distribution gap). Our
approach for solving this problem, named monitored
distribution, tightly integrates lesson distribution with
these decision processes. We describe a case-based
implementation of monitored distribution (ALDS) in a
plan authoring tool suite (HICAP). We evaluate its
utility in a simulated military planning domain. Our
results show that monitored distribution can
significantly improve plan evaluation measures for this
domain
Synthesis, characterization and magnetic properties of Co@Au core-shell nanoparticles encapsulated by nitrogen-doped multiwall carbon nanotubes
"Co/Au bilayer thin films were deposited on Si/SiOx substrates using the magnetron sputtering method and used as a catalytic support to grow forests of aligned nitrogen-doped multiwalled carbon nanotubes (N-MWCNT) via chemical vapor deposition (CVD) at 850 °C, using benzylamine (C6H5CH2NH2) as a carbon and nitrogen source. Interestingly, the resulting N-MWCNT contains Co@Au core-shell nanoparticles located at their tips. We found that the metal particle cores consist of cobalt coated by an Au shell of few nanometers. Magnetic measurements revealed a ferromagnetic behavior of the system composed of Co@Au nanoparticles encapsulated inside N-MWCNT. The results are compared with pristine N-MWNT containing only Co nanoparticles encapsulated in their cores.
The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment
The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in
operation since July 2014. This paper describes the second data release from
this phase, and the fourteenth from SDSS overall (making this, Data Release
Fourteen or DR14). This release makes public data taken by SDSS-IV in its first
two years of operation (July 2014-2016). Like all previous SDSS releases, DR14
is cumulative, including the most recent reductions and calibrations of all
data taken by SDSS since the first phase began operations in 2000. New in DR14
is the first public release of data from the extended Baryon Oscillation
Spectroscopic Survey (eBOSS); the first data from the second phase of the
Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2),
including stellar parameter estimates from an innovative data driven machine
learning algorithm known as "The Cannon"; and almost twice as many data cubes
from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous
release (N = 2812 in total). This paper describes the location and format of
the publicly available data from SDSS-IV surveys. We provide references to the
important technical papers describing how these data have been taken (both
targeting and observation details) and processed for scientific use. The SDSS
website (www.sdss.org) has been updated for this release, and provides links to
data downloads, as well as tutorials and examples of data use. SDSS-IV is
planning to continue to collect astronomical data until 2020, and will be
followed by SDSS-V.Comment: SDSS-IV collaboration alphabetical author data release paper. DR14
happened on 31st July 2017. 19 pages, 5 figures. Accepted by ApJS on 28th Nov
2017 (this is the "post-print" and "post-proofs" version; minor corrections
only from v1, and most of errors found in proofs corrected
Goal Reasoning: Papers from the ACS Workshop
This technical report contains the 14 accepted papers presented at the Workshop on Goal Reasoning,
which was held as part of the 2015 Conference on Advances in Cognitive Systems (ACS-15) in Atlanta,
Georgia on 28 May 2015. This is the fourth in a series of workshops related to this topic, the first of
which was the AAAI-10 Workshop on Goal-Directed Autonomy; the second was the Self-Motivated
Agents (SeMoA) Workshop, held at Lehigh University in November 2012; and the third was the Goal
Reasoning Workshop at ACS-13 in Baltimore, Maryland in December 2013
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