Planning in a dynamic environment is a
complex task that requires several issues to be
investigated in order to manage the associated
search complexity. In this paper, an adaptive
behavior that integrates planning with learning
is presented. The former is performed adopting a
hierarchical approach, interleaved with
execution. The latter, devised to identify new
abstract operators, adopts a chunking technique
on successful plans. Integration between
planning and learning is also promoted by an
agent architecture explicitly designed for
supporting abstraction
