Affiliation:
1. Computer Science Department, Faculty of Computers and Information, Menofiya University, Shebin El Kom, 32511, Egypt.
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. 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.
Publisher
Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
Subject
Electrical and Electronic Engineering,Mechanics of Materials,Civil and Structural Engineering,General Computer Science
Cited by
1 articles.
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