Definition:
- A problem-solving technique that utilizes heuristics (rules of thumb or educated guesses) to guide the search process towards a solution.
- Aims to find a good solution efficiently, but not necessarily the optimal one.
Types of Heuristic Search:
- Direct Heuristic Search:
- Has knowledge of the goal state.
- Uses this knowledge to estimate the distance or cost to the goal.
- Examples:
- A* Search: Combines cost to reach the node (g) and estimated cost to goal (h’) to find the shortest path.
- Greedy Best-First Search: Expands the node closest to the goal based on the heuristic.
- Weak Heuristic Search (Uninformed Search):
- No additional information about the goal state.
- Explores the search space systematically.
- Examples:
- Breadth-First Search (BFS): Explores layer by layer.
- Uniform Cost Search (UCS): Expands the node with the lowest cost.
- Depth-First Search (DFS): Explores as deeply as possible along each branch.
- Iterative Deepening Depth-First Search (IDDFS): Combines DFS with iterative deepening.
- Bidirectional Search: Searches from both the start and goal states simultaneously.
Key Concepts:
- Heuristic Function: Estimates the cost to reach the goal from a given state.
- Admissibility: A heuristic is admissible if it never overestimates the actual cost to the goal.
- Efficiency: Heuristic search aims to find a good solution quickly.
- Completeness: A search algorithm is complete if it is guaranteed to find a solution if one exists.
Advantages of Heuristic Search:
- Can be more efficient than uninformed search.
- Can find good solutions even for complex problems.
Disadvantages of Heuristic Search:
- May not always find the optimal solution.
- The effectiveness depends heavily on the quality of the heuristic function.
Applications:
- Robotics
- Pathfinding and navigation
- Game playing
- Machine learning
- Optimization problems
References:
- Russell, S., and Norvig, P. Artificial Intelligence: A Modern Approach, 4th Edition, 2020, Pearson.
- Rich, E., Knight, K., & Nair, S. B. Artificial Intelligence. McGraw-Hill International.
- Nilsson, N. J. Artificial Intelligence: A New Synthesis. Morgan Kaufmann.
Note: This content was generated with the assistance of Google’s Gemini A