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.
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