Search Algorithms and Problem Solving
Search algorithms are vital in artificial intelligence for solving problems by navigating a space of possible solutions. Uninformed strategies, like Breadth-First Search and Depth-First Search, operate without domain-specific knowledge, while informed strategies, such as A* and Greedy Best-First Search, utilize heuristics for efficient problem solving. The chapter emphasizes the need for effective heuristics and optimization techniques to tackle real-world problems effectively.
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What we have learnt
- A problem-solving agent utilizes states, actions, transition models, goal tests, and path costs.
- Uninformed search strategies explore solution paths without domain knowledge, while informed strategies leverage heuristics for efficiency.
- Effective heuristics enhance search algorithms, and optimization techniques are essential for achieving the best solutions under constraints.
Key Concepts
- -- ProblemSolving Agent
- An agent that is goal-directed and employs search strategies to find a solution to problems.
- -- Heuristic
- A rule of thumb that estimates the cost or distance to reach a goal, used to prioritize search paths in algorithms.
- -- Uninformed Search
- Search strategies that do not use specific information about the problem space and explore blindly.
- -- Informed Search
- Search strategies that utilize heuristics to make decisions that lead to more efficient problem solving.
- -- Admissible Heuristic
- A heuristic that never overestimates the actual cost to reach the goal.
- -- Optimization Techniques
- Methods such as hill climbing, simulated annealing, and genetic algorithms that seek the best possible solution under given constraints.
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