Changes

CNS maintains a range of possible values and keeps expanding the tree until a certain degree of confidence is reached. The confidence is measured by the width of a possible values’ range '''W ''' and a minimum value for conspiracy numbers '''T'''. The purpose of the search is to raise the conspiracy numbers of unlikely values to greater than '''T ''' in order to reduce the range of possible values to below '''W'''. At each turn, CNS tries to disprove either the highest or lowest possible value, which has the highest conspiracy numbers, by expanding one of its conspirators. Then, it recalculates conspiracy numbers and repeats the process until the desired confidence is obtained <ref>[[Quang Vu]], [[Taichi Ishitobi]], [[Jean-Christophe Terrillon]], [[Hiroyuki Iida]] ('''2016'''). ''Using Conspiracy Numbers for Improving Move Selection in Minimax Game-Tree Search''. [http://www.icaart.org/?y=2016 ICAART 2016], [https://pdfs.semanticscholar.org/1bcf/bd2047bc1d74affda11bf2007cac442dd6f4.pdf pdf]</ref>.Since the number of conspiracy numbers per [[Node|node]] '''W''' depends on the number of possible evaluation values, fine grained evaluation, considered necessary for good positional play, yields in into additional computation and thus inefficient search. Further, a final [[Alpha-Beta|alpha-beta]] [[Quiescence Search|quiescence search]] in early CNS implementations was quite expensive due to the lack of [[Bound|bounds]].