  Behavior planning is known to be one of the basic cognitive functions, which
is essential for any cognitive architecture of any control system used in
robotics. At the same time most of the widespread planning algorithms employed
in those systems are developed using only approaches and models of Artificial
Intelligence and don't take into account numerous results of cognitive
experiments. As a result, there is a strong need for novel methods of behavior
planning suitable for modern cognitive architectures aimed at robot control.
One such method is presented in this work and is studied within a special class
of navigation task called smart relocation task. The method is based on the
hierarchical two-level model of abstraction and knowledge representation, e.g.
symbolic and subsymbolic. On the symbolic level sign world model is used for
knowledge representation and hierarchical planning algorithm, PMA, is utilized
for planning. On the subsymbolic level the task of path planning is considered
and solved as a graph search problem. Interaction between both planners is
examined and inter-level interfaces and feedback loops are described.
Preliminary experimental results are presented.

Artificial Intelligence