On 23.02.2016 11:36, Michael Markefka wrote:
whether one could train a DCNN for expected territory
First, some definition of territory must be chosen or stated. Second, you must decide if territory according to this definition can be determined by a neural net meaningfully at all. Third, if yes, do it.
Note that there are very different definitions of territory. The most suitable definition for positional judgement (see Positional Judgement 1 - Territory) is sophisticated and requires a combination of expert rules (specifying for what to detemine, and how to read to determine it) and reading.
A weak definition could predict whether a particular intersections will be territory in the game end's scoring position. Such can be fast for MC or NN, and maybe such is good enough as a very rough approximation for programs. For humans, such is very bad because it neglects different degrees of safety of (potential) territory and the strategic concepts of sacrifice and exchange.
I have also suggested other definitions, but IMO they are less attractive for NN.
-- robert jasiek _______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
