GGP with Advanced Reasoning and Board Knowledge Discovery
  Quality of General Game Playing (GGP) matches suffers from slow
state-switching and weak knowledge modules. Instantiation and Propositional
Networks offer great performance gains over Prolog-based reasoning, but do not
scale well. In this publication mGDL, a variant of GDL stripped of function
constants, has been defined as a basis for simple reasoning machines. mGDL
allows to easily map rules to C++ functions. 253 out of 270 tested GDL rule
sheets conformed to mGDL without any modifications; the rest required minor
changes. A revised (m)GDL to C++ translation scheme has been reevaluated; it
brought gains ranging from 28% to 7300% over YAP Prolog, managing to compile
even demanding rule sheets under few seconds. For strengthening game knowledge,
spatial features inspired by similar successful techniques from computer Go
have been proposed. For they required an Euclidean metric, a small board
extension to GDL has been defined through a set of ground atomic sentences. An
SGA-based genetic algorithm has been designed for tweaking game parameters and
conducting self-plays, so the features could be mined from meaningful game
records. The approach has been tested on a small cluster, giving performance
gains up to 20% more wins against the baseline UCT player. Implementations of
proposed ideas constitutes the core of GGP Spatium - a small C++/Python GGP
framework, created for developing compact GGP Players and problem solvers.
