heuristicdriven

Heuristic-driven refers to processes or systems that are guided predominantly by heuristics—simple, experience-based rules or educated guesses used to make decisions or solve problems quickly. Rather than exhaustive search or guaranteed-optimal optimization, heuristic-driven methods aim for good-enough solutions within practical time and resource constraints. Heuristics may derive from domain knowledge, prior data, or learned approximations, and they can be static or adaptive.

In artificial intelligence and computer science, heuristic-driven techniques are common in search, planning, and optimization. In heuristic search, a heuristic function estimates the cost to reach a goal from a given state; algorithms such as A* use these estimates to prioritize exploration. The distinction between admissible (never overestimates) and non-admissible heuristics affects guarantees of optimality. In other areas, heuristic rules guide scheduling, routing, game AI, and configuration problems.

Origins of heuristics trace to cognitive psychology and the concept of bounded rationality introduced by Herbert Simon, which posits that decision makers operate under information and computation limits, favoring satisficing and rule-of-thumb strategies.

Advantages of heuristic-driven approaches include speed, scalability to large or complex spaces, and robustness when exact models are unavailable. Limitations include the lack of guaranteed optimality, potential bias, sensitivity to the quality of the heuristics, and the risk of poor performance if the heuristics misrepresent the problem.

Related concepts include greedy algorithms, metaheuristics, and bounded rationality, as well as domain-specific heuristic rules. The effectiveness of a heuristic-driven approach depends on the problem structure, the quality and relevance of the heuristics, and the tolerance for suboptimal outcomes.

The term is commonly written as heuristic-driven, though some sources use heuristicdriven as a single word.