IEEE Computational Intelligence Magazine - November 2023 - 11

taking into account the definitions of
IT2-FLSs. This article presents a systematic
solution to these problems by
exploiting the type-reduced set of IT2FLS
via fusing quantile regression and
deep learning (DL) with IT2-FLS. The
uncertainty processing capability ofIT2FLS
depends on employed center-of-sets
calculation methods, while its representation
capability is defined via the structure
of its antecedent and consequent membership
functions. Thus, we present various
parametric IT2-FLSs and define the
learnable parameters of all IT2-FLSs
alongside their constraints to be satisfied
during training. To construct the loss
function, we define a multiobjective loss
and then convert it into a constrained
composite loss composed ofthe log-cosh
loss for accuracy purposes and a tilted loss
for uncertainty representation, which
explicitly uses the type-reduced set. We
also present a DL approach to train IT2FLS
via unconstrained optimizers. In this
context, we present parameterization
tricks for converting the constraint optimization
problem of IT2-FLSs into an
unconstrained one without violating the
definitions offuzzy sets. Finally, we provide
comprehensive comparative results
for hyperparameter sensitivity analysis
and an inter/intramodel comparison on
various benchmark datasets. "
Fuzzy Clustering With Knowledge
Extraction and Granulation, by X. Hu,
Y. Tang, W. Pedrycz, K. Di, J. Jiang,
and Y. Jiang, IEEE Transactions on
Fuzzy Systems, Vol. 31, No. 4, Apr.
2023, pp. 1098-1112.
Digital Object Identifier: 10.1109/
TFUZZ.2022.3195033
" Knowledge-based clustering algorithms
can improve traditional clustering
models by introducing domain knowledge
to identify the underlying data
structure. While there have been several
approaches to clustering with the guidance
ofknowledge tidbits, most of them
mainly focus on numeric knowledge
without considering the uncertain nature
of information. To capture the uncertainty
of information, pure numeric
knowledge tidbits are expanded to
knowledge granules in this article. Then,
two questions arise: how to obtain granular
knowledge and how to use those
knowledge granules in clustering. To the
end, a novel knowledge extraction and
granulation (KEG) method and a granular
knowledge-based fuzzy clustering
model are proposed in this study. First,
inspired by the concept ofnatural neighbors,
an automatic KEG is developed. In
KEG, high-density points are filtered
from the dataset and then merged with
their natural neighbors to form several
dense areas, i.e., granular knowledge.
Furthermore, the granular knowledge
expressed by interval or triangular numbers
is leveraged into the clustering algorithm,
which is the framework of fuzzy
clustering with granular knowledge. To
concretize this model into clustering
algorithms, the classical fuzzy C-Means
clustering algorithm has been selected to
incorporate the granular knowledge produced
by KEG. Then, the corresponding
fuzzy C-Means clustering with interval
knowledge granules (IKG-FCM) and triangular
knowledge granules (TKGFCM)
are proposed. Experiments on
synthetic and real-world datasets demonstrate
that IKG-FCM and TKG-FCM
always achieve better clustering performance
with less time cost, especially on
imbalanced data, compared with stateof-the-art
algorithms. "
IEEE Transactions on Evolutionary
Computation
Explainable Artificial Intelligence by
Genetic Programming: A Survey,byY.
Mei, Q. Chen, A. Lensen, B. Xue,
and M. Zhang, IEEE Transactions on
Evolutionary Computation,Vol.
No. 3,Jun. 2023, pp. 621- 641.
27,
Digital Object Identifier: 10.1109/
TEVC.2022.3225509
" Explainable artificial intelligence
(XAI) has received great interest in the
recent decade, due to its importance in
critical application domains, such as selfdriving
cars, law, and healthcare. Genetic
programming (GP) is a powerful evolutionary
algorithm for machine learning.
Compared with other standard machine
learning models such as neural networks,
the models evolved by GP tend to be
more interpretable due to their model
structure with symbolic components.
However, interpretability has not been
explicitly considered in GP until recently,
following the surge in the popularity of
XAI. This article provides a comprehensive
review ofthe studies on GP that can
potentially improve the model interpretability,
both explicitly and implicitly, as a
byproduct. We group the existing studies
related to explainable artificial intelligence
by GP into two categories. The
first category considers the intrinsic
interpretability, aiming to directly evolve
more interpretable (and effective) models
by GP. The second category focuses on
post-hoc interpretability, which uses GP
to explain other black-box machine
learning models, or explain the models
evolved by GP by simpler models such as
linear models. This comprehensive survey
demonstrates the strong potential of
GP for improving the interpretability of
machine learning models and balancing
the complex tradeoff between model
accuracy and interpretability. "
IEEE Transactions on Games
Procedural Generation of Narrative
Worlds,byJ.T.Balintand R.
Bidarra, IEEE Transactions on
Games,Vol.15,No.2,Jun.2023,
pp. 262-272.
Digital Object Identifier: 10.1109/
TG.2022.3216582
" A narrative world typically consists
of several interrelated locations that, all
together, fully support enacting a given
story. For this, each location in a narrative
world features all the objects as required
there by the narrative, as well as a variety
ofother objects that plausibly describe or
decorate the location. Procedural generation
ofnarrative worlds poses many challenges,
including that, first, it cannot lean
only on domain knowledge (e.g., patterns
ofobjects commonly found in typical
locations), and, second, it involves a
temporal dimension, which introduces
dynamic fluctuations of objects between
locations. In this article, we present a
novel approach for the procedural generation
of narrative worlds, following two
stages: first, a narrative world mold is generated
(only once) for a given story; second,
the narrative world mold is used to
create one (or more) possible narrative
worlds for that story. For each story, its
narrative world mold integrates spatiotemporal
descriptions ofits locations with
NOVEMBER 2023 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 11
IEEE Computational Intelligence Magazine - November 2023

Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - November 2023
