![](bg7.png)
that keeps all the states in memory, which might cause
lack of memory space if the search runs for a long
time.
7 CONCLUSIONS
When concerning simple problems where the initial
state is closer to the goal and few states are generated,
the heuristic proposed by the FF is a useful guide for
search algorithms. Therefore, an agile algorithm such
as EHC can solve problems faster.
However, for more difficult problems in which
many states should be generated and many actions are
required to produce the goal, EHC is inclined to fail
by getting stuck at dead ends. In this case the EHC
execution stops and all the time spent and processing
realized thus far is lost. When this happens, FF starts
a new search from the scratch by using BFS, which
is characterized by a low response time. Also, BFS
needs more space to store the states, which cause it
fail sometimes. In these situations the algorithm HPS-
LRTA* is more efficient, due to the fact that it can es-
cape from a local maximum, avoiding dead ends, and
it also balances the memory space. An improvement
that could be put into practice is to use the concept
of helpful actions in the algorithm, as it is the case
with EHC. Helpful actions filter the most promising
states before the expansion phase, which accelerates
the search process. Also, the authors are planning
to change HPS-LRTA* with respect to the expansion
state. The idea is to generate successors until one
that is better evaluated than the current state, is found.
In doing this we believe that HPS-LRTA* can solve
small problems as fast as EHC.
ACKNOWLEDGEMENTS
This research is supported in part by the Coordination
for the Improvement of Higher Education Personnel
(CAPES), Research Foundation of the State of Minas
Gerais (FAPEMIG) and Faculty of Computing (FA-
COM) from Federal University of Uberlˆandia (UFU).
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