Note that Go computers have exponentially gotten stronger through the monte-carlo method. The most advanced Go computers simply &quot;test&quot; various moves by playing billions of games to completion, and then uses that to test the &quot;probability of winning&quot; with a particular move.<p>In short: use Bayesian logic against the following question: &quot;Calculate the probability of winning given that I&#x27;ve moved a stone at X location&quot;. Use monte-carlo to attempt to estimate hard numbers.<p>I&#x27;m not familiar with your terminology... my school of Go uses the term &quot;Tesuji&quot; (Japanese) to describe what you seem to call &quot;Dingshi&quot; (sounds like a Chinese name to me...). In any case, a computer with strong Tesuji (or maybe Dingshi in your terminology), combined with a monte-carlo method to look for &quot;long-term strategic&quot; moves is what has gotten Go Computers to where they are today.<p>Humans are superior at parallel processing. Go players activate the brain region of <i></i>vision<i></i>, and literally think by seeing the board state. A lot of Go study is seeing patterns and shapes... 4-point bend is life, or Ko in the corner, Crane Nest, Tiger Mouth, the Ladder... etc. etc.<p>Go has probably been so hard for computers to &quot;solve&quot; not because Go is &quot;harder&quot; than Chess (it is... but I don&#x27;t think that&#x27;s the primary reason), but instead because humans brains are innately wired to be better at Go than at Chess. The vision-area of the human&#x27;s brain is very large, and &quot;hacking&quot; the vision center of the brain to make it think about Go is very effective.