Computational Intelligence - August 2016 - 75

∆ Seventh International

* 2017 IEEE International
Conference on Fuzzy Systems
(FUZZ-IEEE 2017)

* 2017 IEEE International Conference
on Data Science and Advanced
Analytics (IEEE DSAA 2017)

December 1-4, 2016
Place: Angkor, Cambodia
General Chair: Jun Wang
Website: https://conference.cs.cityu.
edu.hk/icicip/

July 9-12, 2017
Place: Naples, Italy
General Chairs: Giovanni Acampora,
Bruno Siciliano
Website: http://www.fuzzieee2017.
org/

October 19-21, 2017
Place: Tokyo, Japan
General Chairs: Hiroshi Motoda,
Fosca Giannotti, Tomoyuki Higuchi
Website: http://www.dslab.it.aoyama.
ac.jp/dsaa2017/

* 2016 IEEE Symposium
Series on Computational
Intelligence (IEEE SSCI 2016)

* 2017 IEEE Conference on
Computational Intelligence
and Games (IEEE CIG 2017)

* 2017 IEEE Symposium Series
on Computational Intelligence
(IEEE SSCI 2017)

December 6-9, 2016
Place: Athens, Greece
General Co-Chairs:Yaochu Jin,
Stefanos Kollias
Website: http://ssci2016.cs.surrey.
ac.uk/

August 22-25, 2017
Place: New York, USA
General Chairs: Julian Togelius,
Andy Nealen
Website: TBA

November 27-December 1, 2017
Place: Honolulu, Hawaii
General Chairs: Piero Bonissone,
David Fogel
Website: TBA

* 2017 IEEE Congress on
Evolutionary Computation
(IEEE CEC 2017)

* 2017 IEEE Conference on
Computational Intelligence in
Bioinformatics and Computational
Biology (IEEE CIBCB 2017)

* 2018 IEEE World Congress on
Computational Intelligence
(IEEE WCCI 2018)

June 5-8, 2017
Place: Donostia/San Sebastián, Spain
General Chair: Jose A. Lozano
Website: http://www.cec2017.org

August 23-25, 2017
Place: Manchester, UK
General Chair: Richard Allmendinger
Website: TBA

Conference on Intelligent
Control and Information
Processing (ICICIP 2016)

Publication Spotlight

July 8-13, 2018
Place: Rio de Janeiro, Brazil
General Co-Chairs: Marley Vellasco,
Pablo Estevez
Website: TBA

(continued from page 7)

"Game competitions may involve
different player roles and be score-based
rather than win/loss based. This raises
the issue of how best to draw opponents
for matches in ongoing competitions,
and how best to rank the players in each
role. An example is the Ms Pac-Man
versus Ghosts Competition which
requires competitors to develop software
controllers to take charge of the game's
protagonists: participants may develop
software controllers for either or both
Ms Pac-Man and the team of four
ghosts. In this paper, the authors compare two ranking schemes for win-loss
games, Bayes Elo and Glicko. The
authors convert the game into one of
win-loss ("dominance") by matching
controllers of identical type against the
same opponent in a series of pair-wise
comparisons. This implicitly creates a
"solution concept" as to what constitutes a good player. The authors analyze

how many games are needed under two
popular ranking algorithms, Glicko and
Bayes Elo, before one can infer the
strength of the players, according to the
proposed solution concept, without performing an exhaustive evaluation. The
authors show that Glicko should be the
method of choice for online score-based
game competitions."
IEEE Transactions on Cognitive
and Developmental Systems

Active Learning of Object and Body
Models with Time Constraints on a
Humanoid Robot, by A. Ribes, J. Cerquides,Y. Demiris, and R. L. de Mantaras, IEEE Transactions on Cognitive
and Developmental Systems, Vol. 8, No.
1, March 2016, pp. 26-41.
Digital Object Identifier: 10.1109/
TAMD.2015.2441375

"In this paper, the authors propose
an active learning approach applied to a
music performance imitation scenario.
The humanoid robot iCub listens to a
human performance and then incrementally learns to use a virtual musical
instrument in order to imitate the
given sequence. This is achieved by first
learning a model of the instrument,
needed to locate where the required
sounds are heard in a virtual keyboard
laid out in a tactile interface. Then, a
model of its body capabilities is also
learnt, which serves to establish the
likelihood of success of the actions
needed to imitate the sequence of
sounds and to correct the errors made
by the underlying kinematic controller.
It also uses self-evaluation stages
to provide feedback to the hu--man
instructor, which can be used to guide
its learning process."

august 2016 | IEEE Computational intelligence magazine

https://conference.cs.cityu http://www.fuzzieee2017 http://www.dslab.it.aoyama http://www.edu.hk/icicip/ http://www.ac.jp/dsaa2017/ http://ssci2016.cs.surrey http://www.ac.uk/ http://www.cec2017.org

Table of Contents for the Digital Edition of Computational Intelligence - August 2016