Emotional processes modelling in decision making
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gp-bibliography.bib Revision:1.8098
- @PhdThesis{mahboub:tel-00696675,
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author = "Karim Mahboub",
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title = "Emotional processes modelling in decision making",
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title_fr = "Modelisation des processus emotionnel dans la prise de
decision",
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school = "Universite du Havre",
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year = "2011",
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address = "France",
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month = Nov,
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keywords = "genetic algorithms, genetic programming, ACO, emotion
modelling, problem solving, ant colony algorithms,
linear genetic programming, mod{\`e}les de
l'{\'e}motion, r{\'e}solution de probl{\`e}mes,
algorithmes de colonies de fourmis, programmation
g{\'e}n{\'e}tique lin{\'e}aire",
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hal_id = "tel-00696675",
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hal_version = "v1",
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URL = "https://tel.archives-ouvertes.fr/tel-00696675",
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URL = "https://tel.archives-ouvertes.fr/tel-00696675/file/manuscript.pdf",
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size = "250 pages",
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abstract = "Emotion is inseparable from cognitive processes and
therefore plays a major role in decision making. As a
result, it is becoming increasingly important in
today's scientific research. The aim of this thesis is
to show the advantages of an emotional approach, and to
prove that in certain cases computer models equipped
with artificial emotions prove to be more efficient
than their purely cognitive equivalents. Based on this
observation, two emotional models were realised from
different study perspectives. They underline the impact
of the addition of an emotional dimension in the
elaboration of a fast, adaptive and efficient decision.
The first developed model uses a graph for strategies
representation in order to solve a ten-year-old pupil
mathematics exercise called the Cascades problem.
Emotion is represented there as weighting values in the
graph edges dynamically managed by an ant algorithm.
The tests carried out on two versions, one emotional
and the other one fully cognitive, show that the use of
an emotional model produces a more efficient and
adaptive solving. In addition, a second model named
GAEA aims at simulating a robot equipped with sensors
and effectors and thrown into a prey-predators
environment inside which it must survive. Its behaviour
is determined by its internal program that evolves
thanks to a linear genetic program algorithm
manipulating a population of program individuals.
Results are promising and indicate that the population
produces individuals whose behaviour is more and more
adapted, and whose internal activity is analogous to
the emergence of relevant emotional reactions.",
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notes = "In french. Francais",
- }
Genetic Programming entries for
Karim Mahboub
Citations