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==[[Evaluation]]==
FUSc# classifies [[Middlegame|middlegame]] positions into 32 types considering king placement (k-wing, q-wing, and center) and whether queens are still on the board - addressing [[King Safety|king safety]] issues <ref>[[Marco Block-Berlitz|Marco Block]], Maro Bader, [http://page.mi.fu-berlin.de/tapia/ Ernesto Tapia], Marte Ramírez, Ketill Gunnarsson, Erik Cuevas, Daniel Zaldivar, [[Raúl Rojas]] ('''2008'''). ''Using Reinforcement Learning in Chess Engines''. Concibe Science 2008, [http://www.micai.org/rcs/ Research in Computing Science]: Special Issue in Electronics and Biomedical Engineering, Computer Science and Informatics, Vol. 35, [http://page.mi.fu-berlin.de/block/concibe2008.pdf pdf]</ref>.
Along with one [[Endgame|endgame]] type - 33 types in total, each type has a vector of 1706 positional coefficients ([[Point Value|point values]], [[Piece-Square Tables|piece-square tables]], [[Pawn Structure|pawn structure]]) associated - in total 56298 coefficients, adapted by [[Temporal Difference Learning#TDLeaf|TD-Leaf(λ)]].While a [[Playing Strength|strength ]] improvement of more than 350 rating points after 119 training games on a chess server was reported, this classification scheme of 33 distinct evaluation functions may be diminished due to type transitions with possible [[Evaluation Discontinuity|evaluation discontinuity]].
=See also=
