Limitations of Heuristic Search Techniques
- Heuristic Quality: The power of heuristic search strongly depends on the quality of function the heuristic horizon. If the heuristics are constructed thoughtlessly, then their level of performance may be low or inefficient.
- Space Complexity: The main requirement for some heuristic search algorithms could be a huge memory size in comparison with the others, especially in cases where the search space considerably increases.
- Domain-Specificity: It is often the case that devising efficient heuristics depends on the specifics of the domain, a challenging obstruction to development of generic approaches.
Heuristic Search Techniques in AI
One of the core methods AI systems use to navigate problem-solving is through heuristic search techniques. These techniques are essential for tasks that involve finding the best path from a starting point to a goal state, such as in navigation systems, game playing, and optimization problems. This article delves into what heuristic search is, its significance, and the various techniques employed in AI.
Table of Content
- Understanding Heuristic Search
- Significance of Heuristic Search in AI
- Components of Heuristic Search
- Types of Heuristic Search Techniques
- 1. A Search Algorithm*
- 2. Greedy Best-First Search
- 3. Hill Climbing
- 4. Simulated Annealing
- 5. Beam Search
- Applications of Heuristic Search
- Advantages of Heuristic Search Techniques
- Limitations of Heuristic Search Techniques
- Conclusion