Reduce Artificial Intelligence Planning Effort by using Map-Reduce Paradigm

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