Reduce Artificial Intelligence Planning Effort by using Map-Reduce Paradigm
Mohamed Elkawkagy1, Heba Elbeh2
1Mohamed Elkawkagy*, Computer Science Department, Faculty of Computers and Information, Menofiya University, Shebin El Kom, Egypt.
2Heba Elbeh, Computer Science Department, Faculty of Computers and Information, Menofiya University, Shebin El Kom, Egypt.
Manuscript received on May 03, 2021. | Revised Manuscript received on May 07, 2021. | Manuscript published on May 30, 2021. | PP: 24-32 | Volume-10 Issue-7, May 2021 | Retrieval Number: 100.1/ijitee.G89020510721| DOI: 10.35940/ijitee.G8902.0510721
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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. Pre-processing 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.
Keywords: Artificial Intelligence Planning, Map-Reduce, Hadoop, Big Data.