Heuristic
A heuristic is a problem-solving strategy that relies on practical rules of thumb, intuition, or experience, instead of exhaustive computation. In artificial intelligence, heuristics help algorithms make faster decisions by narrowing down the search space and focusing only on the most promising solutions.
Background
The concept of heuristics became popular in AI during the study of search problems, where finding exact solutions was computationally infeasible. Heuristics do not guarantee optimal results but often yield useful approximations efficiently.
Examples
- A search algorithm*: uses heuristics to estimate the shortest path in navigation systems.
- Machine Learning: feature selection often relies on heuristic methods.
- Decision-Making: in recommendation systems, heuristics can suggest likely relevant items without full computation.
Strengths and weaknesses
- ✅ Useful in domains with large, complex datasets.
- ✅ Provide quick, practical solutions.
- ❌ May lead to suboptimal or biased outcomes if poorly designed.
In artificial intelligence, heuristics act as shortcuts that help algorithms focus on the most promising paths without exhaustively evaluating every possibility. They don’t guarantee perfection, but they make otherwise impossible problems manageable. For instance, in search algorithms, a heuristic might estimate the “distance” to a goal, guiding the algorithm to explore smarter rather than harder.
Heuristics are especially valuable in combinatorial optimization problems, where the number of possible solutions grows exponentially. Scheduling airline crews, solving puzzles like Sudoku, or navigating a maze can all benefit from heuristic strategies.
One challenge is that heuristics are only as good as the assumptions behind them. A poorly designed heuristic can mislead the algorithm, resulting in longer runtimes or suboptimal results. This trade-off between speed and accuracy is why heuristics are often combined with other methods, such as metaheuristics (genetic algorithms, simulated annealing), to balance exploration and exploitation.
📚 Further Reading
- Pearl, J. (1984). Heuristics: Intelligent Search Strategies.
- Gigerenzer, G., Todd, P. (1999). Simple Heuristics That Make Us Smart.
