Uninformed Search

Uninformed search (also called blind search) is a class of search algorithms that explore a problem space without problem-specific knowledge to guide them. Unlike informed search, which uses a heuristic function to direct exploration, uninformed search treats all paths as equally promising and explores systematically. The canonical examples are breadth-first search, depth-first search, and Dijkstra's algorithm — which, despite its optimality, is uninformed because it uses only the cost-so-far, not an estimate of remaining cost.

The distinction between informed and uninformed search is not about completeness or optimality. It is about the epistemic stance of the algorithm. An uninformed search algorithm admits total ignorance about the goal's location and explores accordingly. This ignorance is not a weakness when the search space is small or when no reliable heuristic exists. But in large spaces, uninformed search is computationally intractable — which is precisely why bounded rationality demands heuristics in both natural and artificial systems.