% Figure 22.5 An implementation of the alpha-beta algorithm. % The alpha-beta algorithm alphabeta( Pos, Alpha, Beta, GoodPos, Val) :- moves( Pos, PosList), !, boundedbest( PosList, Alpha, Beta, GoodPos, Val); staticval( Pos, Val). % Static value of Pos boundedbest( [Pos | PosList], Alpha, Beta, GoodPos, GoodVal) :- alphabeta( Pos, Alpha, Beta, _, Val), goodenough( PosList, Alpha, Beta, Pos, Val, GoodPos, GoodVal). goodenough( [], _, _, Pos, Val, Pos, Val) :- !. % No other candidate goodenough( _, Alpha, Beta, Pos, Val, Pos, Val) :- min_to_move( Pos), Val > Beta, ! % Maximizer attained upper bound ; max_to_move( Pos), Val < Alpha, !. % Minimizer attained lower bound goodenough( PosList, Alpha, Beta, Pos, Val, GoodPos, GoodVal) :- newbounds( Alpha, Beta, Pos, Val, NewAlpha, NewBeta), % Refine bounds boundedbest( PosList, NewAlpha, NewBeta, Pos1, Val1), betterof( Pos, Val, Pos1, Val1, GoodPos, GoodVal). newbounds( Alpha, Beta, Pos, Val, Val, Beta) :- min_to_move( Pos), Val > Alpha, !. % Maximizer increased lower bound newbounds( Alpha, Beta, Pos, Val, Alpha, Val) :- max_to_move( Pos), Val < Beta, !. % Minimizer decreased upper bound newbounds( Alpha, Beta, _, _, Alpha, Beta). % Otherwise bounds unchanged betterof( Pos, Val, Pos1, Val1, Pos, Val) :- % Pos better than Pos1 min_to_move( Pos), Val > Val1, ! ; max_to_move( Pos), Val < Val1, !. betterof( _, _, Pos1, Val1, Pos1, Val1). % Otherwise Pos1 better