IEEE Computational Intelligence Magazine - November 2019 - 9

the scope and properties of the datasets.
In this sense, new proposals on the topic
often focus on a given context, aiming at
improving the related state-of-the-art
approaches. Data complexity metrics
have been traditionally used to determine
the inner characteristics of datasets. This
way, researchers are able to categorize the
problems in different scenarios. Then, this
taxonomy can be applied to determine
inner characteristics of the datasets in
order to determine intervals of good and
bad behavior for a given classifier. In this
paper, the authors will take advantage of
the data complexity metrics in order to
design a fuzzy metaclassifier. The final
goal is to create decision rules based on
the inner characteristics of the data to
apply a different version of the fuzzy classifier for a given problem. To do so, the
authors will make use of the FARC-HD
classifier, an evolutionary fuzzy system
that has led to different extensions in the
specialized literature. Experimental results
show the goodness of this novel approach
as it is able to outperform all versions of
FARC-HD on a wide set of problems,
and obtain competitive results (in terms
of performance and interpretability)
versus two selected state-of-the-art
rule-based classification system, C4.5
and FURIA."
A Constrained Representation Theorem
for Well-Shaped Interval Type-2 Fuzzy
Sets, and the Corresponding Constrained
Uncertainty Measures, by D. Wu, H.-T.
Zhang, and J. Huang, IEEE Transactions on Fuzzy Systems, Vol. 27, No.
6, June 2019, pp. 1237-1251.
Digital Object Identifier: 10.1109/
TFUZZ.2018.2874018
"The representation theorem for
interval type-2 fuzzy sets (IT2 FSs),
proposed by Mendel and John, states
that an IT2 FS is a combination of all
its embedded type-1 (T1) FSs, which
can be nonconvex and/or subnormal.
These nonconvex and/or subnormal
embedded T1 FSs are included in
developing many theoretical results for
IT2 FSs, including uncertainty measures, the linguistic weighted averages
(LWAs), the ordered LWAs (OLWAs),

the linguistic weighted power means
(LWPMs), etc. However, convex and
normal T1 FSs are used in most fuzzy
logic applications, particularly computing with words. In this paper, the
authors propose a constrained representation theorem (CRT) for well-shaped
IT2 FSs using only its convex and normal embedded T1 FSs, and show that
IT2 FSs generated from three word
encoding approaches and four computing with words engines (LWAs, OLWAs,
LWPMs, and perceptual reasoning) are
all well-shaped IT2 FSs. The authors
also compute five constrained uncertainty measures (centroid, cardinality,
fuzziness, variance, and skewness) for
well-shaped IT2 FSs using the CRT.
The CRT and the associated constrained uncertainty measures can be
useful in computing with words, IT2
fuzzy logic system design using the
principles of uncertainty, and measuring
the similarity between two well-shaped
IT2 FSs.
IEEE Transactions on
Evolutionary Computation

Data-Driven Evolutionary Optimization: An Overview and Case Studies, by
Y. Jin, H. Wang, T. Chugh, D. Guo,
and K. Miettinen, IEEE Transactions
on Evolutionary Computation, Vol. 23,
No. 3, June 2019, pp. 442-458.
Digital Object Identifier: 10.1109/
TEVC.2018.2869001
"Most evolutionary optimization
algorithms assume that the evaluation of
the objective and constraint functions is
straightforward. In solving many realworld optimization problems, however,
such objective functions may not exist.
Instead, computationally expensive numerical simulations or costly physical
experiments must be performed for fitness evaluations. In more extreme cases,
only historical data are available for performing optimization and no new data
can be generated during optimization.
Solving evolutionary optimization
problems driven by data collected in
simulations, physical experiments, production processes, or daily life are

termed data-driven evolutionary optimization. In this paper, the authors provide a taxonomy of different data
driven evolutionary optimization problems, discuss main challenges in datadriven evolutionary optimization with
respect to the nature and amount of
data, and the availability of new data
during optimization. Real-world application examples are given to illustrate
different model management strategies
for different categories of data-driven
optimization problems."
IEEE Transactions on Games

Building a Planner: A Survey of Planning Systems Used in Commercial Video
Games, by X. Neufeld, S. Mostaghim,
D. L. Sancho-Pradel, and S. Brand,
IEEE Transactions on Games, Vol. 11,
No. 2, June 2019, pp. 91-108.
Digital Object Identifier: 10.1109/
TG.2017.2782846
"In the last decade, many commercial video games have used planners
instead of classical behavior trees or
finite state machines to define agent
behaviors. Planners allow looking ahead
in time and can prevent some problems
of purely reactive systems. Furthermore,
some of them allow coordination of
multiple agents. However, implementing
a planner for highly dynamic environments such as video games is a difficult
task. This paper aims to provide an overview of different elements of planners
and the problems that developers might
have when dealing with them. We identify the major areas of plan creation and
execution, trying to guide developers
through the process of implementing a
planner and discuss possible solutions for
problems that may arise in the following
areas: environment, planning domain,
goals, agents, actions, plan creation, and
plan execution processes. Giving insights
into multiple commercial games, we
show different possibilities of solving
such problems and discuss which solutions are better suited under specific circumstances, and why some academic
approaches find a limited application in
the context of commercial titles."

NOVEMBER 2019 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE

IEEE Computational Intelligence Magazine - November 2019

Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - November 2019