SSS*

SSS* is a search algorithm, introduced by George Stockman in 1979, that conducts a state space search traversing a game tree in a best-first fashion similar to that of the A* search algorithm. SSS* is a search algorithm, introduced by George Stockman in 1979, that conducts a state space search traversing a game tree in a best-first fashion similar to that of the A* search algorithm. SSS* is based on the notion of solution trees. Informally, a solution tree can be formed from any arbitrary game tree by pruning the number of branches at each MAX node to one. Such a tree represents a complete strategy for MAX, since it specifies exactly one MAX action for every possible sequence of moves made by the opponent. Given a game tree, SSS* searches through the space of partial solution trees, gradually analyzing larger and larger subtrees, eventually producing a single solution tree with the same root and Minimax value as the original game tree. SSS* never examines a node that alpha-beta pruning would prune, and may prune some branches that alpha-beta would not. Stockman speculated that SSS* may therefore be a better general algorithm than alpha-beta. However, Igor Roizen and Judea Pearl have shown that the savings in the number of positions that SSS* evaluates relative to alpha/beta is limited and generally not enough to compensate for the increase in other resources (e.g., the storing and sorting of a list of nodes made necessary by the best-first nature of the algorithm). However, Aske Plaat, Jonathan Schaeffer, Wim Pijls and Arie de Bruin have shown that a sequence of null-window alpha-beta calls is equivalent to SSS* (i.e., it expands the same nodes in the same order) when alpha-beta is used with a transposition table, as is the case in all game-playing programs for chess, checkers, etc. Now the storing and sorting of the OPEN list were no longer necessary. This allowed the implementation of (an algorithm equivalent to) SSS* in tournament quality game-playing programs. Experiments showed that it did indeed perform better than Alpha-Beta in practice, but that it did not beat NegaScout. The reformulation of a best-first algorithm as a sequence of depth-first calls prompted the formulation of a class of null-window alpha-beta algorithms, of which MTD-f is the best known example. There is a priority queue OPEN that stores states ( J , s , h ) {displaystyle (J,s,h)} or the nodes, where J {displaystyle J} - node identificator (Dewey's notation is used to identify nodes, ϵ {displaystyle epsilon } is a root), s ∈ { L , S } {displaystyle sin {L,S}} - state of the node J {displaystyle J} (L - the node is live, which means it's not solved yet and S - the node is solved), h ∈ ( − ∞ , ∞ ) {displaystyle hin (-infty ,infty )} - value of the solved node. Items in OPEN queue are sorted descending by their h {displaystyle h} value. If more than one node has the same value of h {displaystyle h} , a node left-most in the tree is chosen. Γ {displaystyle Gamma } operator for p = ( J , s , h ) {displaystyle p=(J,s,h)} is defined in the following way: