Computational Intelligence - February 2015 - 17

"The authors propose a Growing
Type-2 Fuzzy Classifier (GT2FC) for
online rule learning from real-time data
streams. In online rule learning, data can
dynamically change over time becoming
potentially non-stationary. To accommodate dynamic change, GT2FC relies on a
new semi-supervised online learning
algorithm called Growing Gaussian Mixture Model (2G2M). In particular, 2G2M
is used to generate the type-2 fuzzy
membership functions to build the
type-2 fuzzy rules. GT2FC is designed to
accommodate data online and to reconcile labeled and unlabeled data using selflearning. Moreover, GT2FC maintains
low complexity of the rule base using
online optimization and feature selection
mechanisms. GT2FC is tested on data
obtained from an ambient intelligence
application, where the goal is to exploit
sensed data for monitoring the living
space on behalf of the inhabitants.
Because sensors are prone to faults and
noise, type-2 fuzzy modeling is very suitable for dealing with such an application.
Thus, GT2FC offers the advantage of
dealing with uncertainty in addition to
self-adaptation in an online manner."
IEEE Transactions on
Evolutionary Computation

Differential Evolution with Dynamic
Parameters Selection for Optimization
Problems, by R.A. Sarker, S.M. Elsayed,
and T. Ray, IEEE Transactions on Evolutionary Computation, Vol. 18, No. 5,
October 2014, pp. 689-707.
Digital Object Identifier: 10.1109/
TEVC.2013.2281528
"The success of DE is highly dependent on its search operators and control
parameters, which are often decided a
priori. Selection of the parameter values
is itself a combinatorial optimization
problem. In this paper, a differential evolution algorithm is proposed that uses a
new mechanism to dynamically select
the best performing combinations of
parameters (amplification factor, crossover rate and the population size) for a
problem. The performance of the algorithm is judged by solving three well-

known sets of optimization test problems
(two constrained and one unconstrained). The results demonstrate that
the proposed algorithm not only saves
the computational time, but also shows
better performance over the state-ofthe-art algorithms."
Genetic Algorithms for Evolving Computer Chess Programs, by O.E. David, H.J.
van den Herik, M. Koppel, and N.S.
Netanyahu, IEEE Transactions on Evolutionary Computation, Vol. 18, No. 5,
October 2014, pp. 779-789.
Digital Object Identifier: 10.1109/
TEVC.2013.2285111
"This paper demonstrates the use of
genetic algorithms for evolving (1) a
grandmaster-level evaluation function
and (2) a search mechanism for a chess
program. The evaluation function of the
program is evolved by learning from databases of (human) grandmaster games.
The organisms are initially evolved to
mimic the behavior of human grandmasters, and then these organisms are
further improved through coevolution.
Learning from tactical test suites evolves
the search mechanism. The results show
the evolved program outperforms a twotime World Computer Chess Champion
and is on a par with other leading computer chess programs."
IEEE Transactions on
Computational Intelligence
and AI in Games

A Computational Model of Plan-Based
Narrative Conflict at the Fabula Level, by
S.G. Ware, R.M. Young, B. Harrison,
and D.L. Roberts, IEEE Transactions on
Computational Intelligence and AI in
Games, Vol. 6, No. 3, September 2014,
pp. 271-288.
Digital Object Identifier: 10.1109/
TCIAIG.2013.2277051
"Conflict is an essential element of
interesting stories and of many genres of
game. In this paper, the authors operationalize a narratological definition of
conflict and extend established narrative
planning techniques to incorporate this

definition. The conflict partial order
causal link planning algorithm (CPOCL)
allows narrative conflict to arise in a plan
while maintaining causal soundness and
character believability. Seven dimensions
of conflict are defined in terms of this
algorithm's knowledge representation.
The first three-participants, reason, and
duration-are discrete values which
answer the "who?" "why?" and "when?"
questions, respectively. The last four-balance, directness, stakes, and resolution-are
continuous values which describe important narrative properties that can be used
to select conflicts based on the author's
purpose. Results of two empirical studies
are presented which validate the model
of these narrative phenomena and demonstrations are given of the different
kinds of story that can be produced."
IEEE Transactions on Autonomous
Mental Development

Successive Developmental Levels of Autobiographical Memory for Learning Through
Social Interaction, by G. Pointeau, M.
Petit, and P.F. Dominey, IEEE Transactions on Autonomous Mental Development, Vol. 6, No. 3, September 2014,
pp. 200-212.
Digital Object Identifier: 10.1109/
TAMD.2014.2307342
"A developing cognitive system will
ideally acquire knowledge of its interaction in the world, and will be able to use
that knowledge to construct a scaffolding
for progressively structured levels of
behavior. The current research implements and tests an autobiographical
memory system by which a humanoid
robot, the iCub, can accumulate its experience in interacting with humans, and
extract regularities that characterize this
experience. This knowledge is then used
in order to form composite representations of common experiences. We first
apply this to the development of knowledge of spatial locations, and relations
between objects in space. We then demonstrate how this can be extended to
temporal relations between events, including "before" and "after," which structure
the occurrence of events in time."

february 2015 | Ieee ComputatIonal IntellIgenCe magazIne

Table of Contents for the Digital Edition of Computational Intelligence - February 2015