Algorithm using MAC and bloom filters

BELOW IS THE ORIGINAL REQUIREMENT HOWEVER 60% of the paper is already completed. Right now I need 20 pages added to the work. Revision instructions will be uploaded. I need the work in 12 days. I only need it to be revised according to the instructions below. It's only 20 pages. For better understanding of the 60 pages completed, read the original instructions right at the bottom.. However pls note you don't have to do anything on scratch but just follow the revision listed immediately below. ................................................... REVISION INSTRUCTIONS Use less AC and SAC more. The bloom filters need more work, it is just 1 page! I should use case studies and after that use it with new MAC/BF. I should use existing solver (already existing codes) and use MAC and use bloom filters on top of it. For example choco, mistral or gecode And I could use sedo code in the text. Then test it and compare with sac and MAC. Basically, I do not need the theoretical framework its just bloom filters and MAC. The code for Mac is already there just put the BF on top of it. Thats what i want me to use for the code. I only need 20 pages more as almost 60% is completed. ORIGINAL INSTRUCTIONS(only for reference) The order is for: 1. full report and discussion of theoretical new algorithm using MAC (and possibly Bloom filters) 3. simulation results of the proposd new algorithm I do not have new algorithm .................................................... Need to be written in latex. It will focus on MAC"Maintaining Arc Consistency" and Bloom filter and use the best extend such as Counting Bloom fillter, Spectral Bloom fillter, Bloom fillter, Compressed Bloom lter and Distance-sensitive Bloom filter are presented. and that to reduce the time and the offer Description: Constraints are a convenient paradigm for defining, representing, and solving many problems in industry, engineering, planning, scheduling, and academia. Solving constraint satisfaction problems is usually done by some for of depth-first srearch. A lot of time is wasted by visiting previously visited nodes in the search tree. Bloom filters are a space-efficient data structure that can answer queries about set membership. Negative answers are always correct, but positive answers may be wrong. In this project you will investigate the use of Bloom filters to detect previously visited search state