Reduce Artificial Intelligence Planning Effort by using Map-Reduce Paradigm
Creators
- 1. Computer Science Department, Faculty of Computers and Information, Menofiya University, Shebin El Kom, 32511, Egypt.
- 2. Computer Science Department, Faculty of Computers and Information, Menofiya University, Shebin El Kom, 32511, Egypt.
Contributors
- 1. Publisher
Description
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.
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G89020510721.pdf
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Related works
- Is cited by
- Journal article: 2278-3075 (ISSN)
Subjects
- ISSN
- 2278-3075
- Retrieval Number
- 100.1/ijitee.G89020510721