TITLE:
---------------------------------------------------------------------
Speeding Up Learning in Real-time Search through Parallel Computing

ABSTRACT:
---------------------------------------------------------------------
Real-time search algorithms solve the problem of
path planning, regardless the size and complexity of the maps,
and the massive presence of entities in the same environment.
In such methods, the learning step aims to avoid local minima
and improve the results for future searches, ensuring the
convergence to the optimal path when the same planning
task is solved repeatedly. However, performing search in a
limited area due to real-time constraints makes the run to
convergence a lengthy process. In this work, we present a
parallelization strategy that aims to reduce the time to 
convergence, maintaining the real-time properties of the search.
The parallelization technique consists on using auxiliary searches
without the real-time restrictions present in the main search.
In addition, the same learning is shared by all searches. The
empirical evaluation shows that even with the additional cost
required to coordinate the auxiliary searches, the reduction in
time to convergence is significant, showing gains from searches
occurring in environments with fewer local minima to larger
searches on complex maps, where performance improvement
is even better.