Improving AI Planning using Map Reduce
Mohamed Elkawkagy1, Heba Elbeh2
1Mohamed Elkawkagy*, Computer Science Department, Community College, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
2Heba Elbeh, Computer Science Department, Community College, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
Manuscript received on January 16, 2020. | Revised Manuscript received on January 26, 2020. | Manuscript published on February 10, 2020. | PP: 615-618 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1309029420/2020©BEIESP | DOI: 10.35940/ijitee.D1309.029420
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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: Today, the Landmark concept is adapted from the classical planning to work in hierarchical task network planning. It was shown how it is used to extracts landmark literals from a given hierarchical planning domain and problem description and then use these literals to update the the planning domain by ruling out the irrelevant tasks and methods before the actual planning is performed. In this paper, we compine the landmark concept with the Map-reduce framework to increase the performance of the planning process. Our empirical evaluation shows that the combination between landmark and Map-Reduce framework dramatically improves performance of the planning process.
Keywords: Classical Planning, HTN Planning, Search strateg
Scope of the Article: Problem Solving and Planning