Britannica
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How did the General Problem Solver approach puzzles?
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artificial intelligence
Image generated by the Stable Diffusion model from the prompt “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings,” which is the definition of artificial intelligence in the Encyclopædia Britannica article on the subject. Stable Diffusion is trained on a large set of images paired with textual descriptions and uses natural language processing to generate an image.
The General Problem Solver (GPS), developed by Allen Newell, Herbert A. Simon, and J. Clifford Shaw, approached puzzles and well-defined problems using a means-ends analysis strategy.12
Rather than relying solely on simple trial and error, GPS functioned by identifying an end goal and recursively breaking it down into smaller, manageable subgoals. The process involved the following steps:12
- Goal Decomposition: The program examined the desired end state and generated subgoals and action plans to overcome specific obstacles.2
- Means-Ends Assessment: After solving each subproblem, the system conducted an assessment to determine if it had moved closer to the intended final solution.2
- Heuristic Application: GPS recursively applied heuristic techniques to navigate the problem space, utilizing a combination of forward and backward search techniques to evaluate the difference between the current state and the goal state.2
While GPS was capable of solving a variety of puzzles, mathematical theorems, and logical proofs, it was limited by its lack of learning capability, meaning its intelligence was derived entirely from the information explicitly provided by its programmers.1