Problem Space Search: Hill Climbing vs Simulated Annealing

Newell and Simon modelled problem solving as search through a "problem space" — states connected by operators, moving from a start toward a goal. This simulator renders that idea as a 2D fitness landscape built from overlapping Gaussian peaks, with two agents searching it side by side from the same starting state: one running greedy hill climbing, the other simulated annealing. Watch hill climbing get trapped the instant it reaches a local maximum, and watch simulated annealing's cooling temperature let it tolerate temporarily worse moves — a direct computational parallel to the incubation and functional-fixedness effects the accompanying article covers — before settling onto (or near) the true global optimum, with a live chart tracking both agents' objective value step by step.