Ascertaining the Play Outcome using the Single Conspiracy Number in GoBang

Analyzing the effect of value change in the MIN/-MAX search algorithm towards gameplay can be challenging. The single conspiracy number (SCN) is an indicator that can reflect such information. In this paper, SCN is utilized to identify different factors and analyze patterns that influenced a 5-in-a-row game called GoBang. SCN values from various GoBang games were calculated and analyzed. A specific GoBang dataset called the "chess manuals" is used as the simulation test-bed to reflect real game situations. Results show that such a method is proven to indicate different game situations, leading to the ability to ascertain the outcome of the play, given the right threshold, relative to the MIN/MAX evaluation value.