Matches in DBpedia 2014 for { ?s ?p General Problem Solver or G.P.S. was a computer program created in 1959 by Herbert A. Simon, J.C. Shaw, and Allen Newell intended to work as a universal problem solver machine. Any formalized symbolic problem can be solved, in principle, by GPS. For instance: theorems proof, geometric problems and chess playing. It was based on Simon and Newell's theoretical work on logic machines. GPS was the first computer program which separated its knowledge of problems (rules represented as input data) from its strategy of how to solve problems (a generic solver engine). It was implemented in the low-level IPL programming language.While GPS solved simple problems such as the Towers of Hanoi that could be sufficiently formalized, it could not solve any real-world problems because search was easily lost in the combinatorial explosion of intermediate states.The user defined objects and operations that could be done on the objects, and GPS generated heuristics by Means-ends analysis in order to solve problems. It focused on the available operations, finding what inputs were acceptable and what outputs were generated. It then created subgoals to get closer and closer to the goal.The GPS paradigm eventually evolved into the Soar architecture for Artificial Intelligence.. }
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- General_Problem_Solver abstract "General Problem Solver or G.P.S. was a computer program created in 1959 by Herbert A. Simon, J.C. Shaw, and Allen Newell intended to work as a universal problem solver machine. Any formalized symbolic problem can be solved, in principle, by GPS. For instance: theorems proof, geometric problems and chess playing. It was based on Simon and Newell's theoretical work on logic machines. GPS was the first computer program which separated its knowledge of problems (rules represented as input data) from its strategy of how to solve problems (a generic solver engine). It was implemented in the low-level IPL programming language.While GPS solved simple problems such as the Towers of Hanoi that could be sufficiently formalized, it could not solve any real-world problems because search was easily lost in the combinatorial explosion of intermediate states.The user defined objects and operations that could be done on the objects, and GPS generated heuristics by Means-ends analysis in order to solve problems. It focused on the available operations, finding what inputs were acceptable and what outputs were generated. It then created subgoals to get closer and closer to the goal.The GPS paradigm eventually evolved into the Soar architecture for Artificial Intelligence.".