Gil E. Fuchs,
an Israeli computer scientist with a Ph.D. from University of California, Santa Cruz in 2004. At UCSC he researched along with Robert Levinson on conceptual graphs (CGs), and pattern-weight pairs (pws) to represent knowledge for search and planning. Pws were the basic representational entity of Morph III, a self learningpattern-oriented chess program [1], and were also applied to other board games such as Diplomacy[2]. Patterns are represented as conceptual position graphs, the edges being attack relationships and the nodes being pieces and important squares. Pattern weights are adjusted by temporal-difference learning in self-play, in which each pattern has its own learning rate set by simulated annealing (i.e., the more frequently a pattern is updated, the slower becomes its learning rate). Morph evaluates a position by combining the weights of all matched patterns into a single evaluation function score, and performs only a one-ply search [3].
an Israeli computer scientist with a Ph.D. from University of California, Santa Cruz in 2004. At UCSC he researched along with Robert Levinson on conceptual graphs (CGs), and pattern-weight pairs (pws) to represent knowledge for search and planning. Pws were the basic representational entity of Morph III, a self learning pattern-oriented chess program [1], and were also applied to other board games such as Diplomacy [2]. Patterns are represented as conceptual position graphs, the edges being attack relationships and the nodes being pieces and important squares. Pattern weights are adjusted by temporal-difference learning in self-play, in which each pattern has its own learning rate set by simulated annealing (i.e., the more frequently a pattern is updated, the slower becomes its learning rate). Morph evaluates a position by combining the weights of all matched patterns into a single evaluation function score, and performs only a one-ply search [3].
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