Open AccessImproving AI Planning using Map Reduce
Author(s) -
Mohamed Elkawkagy,
Heba Elbeh
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.d1309.029420
Subject(s) - landmark , computer science , task (project management) , domain (mathematical analysis) , process (computing) , artificial intelligence , automated planning and scheduling , machine learning , systems engineering , engineering , mathematics , mathematical analysis , operating system
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.