Unit 2: Informed Heuristic Search Strategies Flashcards
What is a heuristic function in AI?
A heuristic function estimates the cost to reach the goal from a given node, guiding search algorithms towards promising paths.
Describe the Greedy Best-First Search algorithm.
Greedy Best-First Search expands the most promising node based solely on heuristic evaluation, focusing on immediate benefits without considering overall path cost.
What is the A* algorithm and its formula?
The A* algorithm combines path cost and heuristic to determine the best node to expand, using the formula f(n)=g(n)+h(n)f(n)=g(n)+h(n), where:
g(n) = cost to reach node nn
h(n)h(n) = estimated cost from nn to the goal.
What are local search algorithms?
Local search algorithms explore the search space by moving from one solution to another within the neighborhood, useful for optimization problems.
Define optimization problems in AI.
Optimization problems require finding the best solution from all possible solutions, often involving the maximization or minimization of an objective function.
Explain Hill-Climbing Search.
Hill-Climbing Search is a local search algorithm that continuously moves towards increasing value (or decreasing cost) but may get stuck in local maxima.
What is Local Beam Search?
Local Beam Search maintains multiple states (beams) and explores them simultaneously, helping to avoid local maxima by diversifying the search.
Describe Genetic Algorithms.
Genetic Algorithms are inspired by natural selection, using techniques such as selection, crossover, and mutation to evolve solutions over generations.
Q: What are some experiments related to search strategies in AI?
A:
Finding a peak element in an array: Implement efficient algorithms to identify a peak. Finding the square root of the peak number: Apply numerical methods for computation. Shortest path finding using A* and Best First Search: Utilize these algorithms for pathfinding scenarios.
Q: How do you handle searching with partial observations?
A: Searching with partial observations involves using strategies where the agent has incomplete information, requiring sophisticated algorithms to make decisions based on limited data.
Q: What is the performance measure for a taxi driver?
A: The performance measure includes safety of passengers and driver, timeliness of reaching the destination, fuel efficiency, customer satisfaction, and adherence to traffic laws and regulations.
Q: What are the key environmental factors for a taxi driver?
A: The environment consists of city streets, highways, traffic signals, signs, road markings, other vehicles and pedestrians, weather conditions, and passengers with their destinations.
Q: List the actuators used by a taxi driver.
A: Actuators include the steering wheel, accelerator and brake pedals, gearshift, horn, and turn signals.
Q: What sensors are utilized by a taxi driver?
A: Sensors include cameras (rear-view and lane-keeping), radar and lidar (for detecting obstacles), GPS (for navigation), speedometer, odometer, traffic signal sensors, and microphones (for passenger interactions).
Q: What are the performance measures in medical diagnosis?
A: Performance measures include accuracy of diagnosis, speed of diagnosis, patient satisfaction, correctness of treatment recommendations, and reduction in misdiagnoses and false positives/negatives.
