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Fail-Soft is a term related to an Alpha-Beta like search. Returned scores might be outside the bounds:


In his 1983 paper Another optimization of alpha-beta search [1], John Philip Fishburn introduced Fail-Soft Alpha-Beta as an improvement of Fail-Hard without any extra work [2]. Fail-Soft has the reputation for searching less nodes than Fail-Hard, but might also require some care regarding to search instability issues in conjunction with transposition tables and various pruning-, reduction- and extension techniques.

Mate Scores

In Chrilly Donninger's initial null move pruning implementation there was a deep search extension [3], if the null move was refuted by a mate attack, thus relying on Fail-Soft of a null window search, where many "random" moves may refute the null-move with or without score in the mate range.

See also


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External Links


  1. ^ John Philip Fishburn (1983). Another optimization of alpha-beta search. SIGART Bulletin, Issue 84, pdf
  2. ^ Jean-Christophe Weill (1991). Experiments With the NegaC* Search - An Alternative for Othello Endgame Search. Heuristic Programming in AI 2
  3. ^ Chrilly Donninger (1993). Null Move and Deep Search: Selective-Search Heuristics for Obtuse Chess Programs. ICCA Journal, Vol. 16, No. 3

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