While several approaches have been developed to
enhance the efficiency of hierarchical Artificial Intelligence
planning (AI-planning), complex problems in AI-planning are
challenging to overcome. To find a solution plan, the hierarchical
planner produces a huge search space that may be infinite. A
planner whose small search space is likely to be more efficient
than a planner produces a large search space. In this paper, we
will present a new approach to integrating hierarchical
AI-planning with the map-reduce paradigm. In the mapping part,
we will apply the proposed clustering technique to divide the
hierarchical planning problem into smaller problems, so-called
sub-problems. A pre-processing technique is conducted for each
sub-problem to reduce a declarative hierarchical planning
domain model and then find an individual solution for each
so-called sub-problem sub-plan. In the reduction part, the
conflict between sub-plans is resolved to provide a general
solution plan to the given hierarchical AI-planning problem. Preprocessing phase helps the planner cut off the hierarchical
planning search space for each sub-problem by removing the
compulsory literal elements that help the hierarchical planner
seek a solution. The proposed approach has been fully
implemented successfully, and some experimental results
findings will be provided as proof of our approach's substantial
improvement inefficiency.