Anticipatory Learning Classifier Systems describes the state of the
art of anticipatory learning classifier systems-adaptive rule
learning systems that autonomously build anticipatory environmental
models. An anticipatory model specifies all possible action-effects
in an environment with respect to given situations. It can be used
to simulate anticipatory adaptive behavior.
Anticipatory Learning Classifier Systems highlights how
anticipations influence cognitive systems and illustrates the use
of anticipations for (1) faster reactivity, (2) adaptive behavior
beyond reinforcement learning, (3) attentional mechanisms, (4)
simulation of other agents and (5) the implementation of a
motivational module. The book focuses on a particular evolutionary
model learning mechanism, a combination of a directed specializing
mechanism and a genetic generalizing mechanism. Experiments show
that anticipatory adaptive behavior can be simulated by exploiting
the evolving anticipatory model for even faster model learning,
planning applications, and adaptive behavior beyond reinforcement
learning.
Anticipatory Learning Classifier Systems gives a detailed
algorithmic description as well as a program documentation of a C++
implementation of the system. It is an excellent reference for
researchers interested in adaptive behavior and machine learning
from a cognitive science perspective as well as those who are
interested in combining evolutionary learning mechanisms for
learning and optimization tasks.
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