Artificial Intelligence Search Heuristics
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Explore search algorithms in Artificial Intelligence, comparing BFS vs. DFS, Uniform Cost Search, A* algorithms, and local search methods like Hill Climbing and Genetic Algorithms. Understand key concepts and applications of AI search. Discover the importance of heuristics in optimizing search efficiency.
Artificial Intelligence Search Heuristics
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Presentation Transcript
CHAPTER 2 SEARCHHEURISTIC
QUESTION ???? • What is Artificial Intelligence? The study of systems that act rationally • What does rational mean? Given its goals and prior knowledge, a rational agent should: 1. Use the information available in new observations to update its knowledge, and 2. Use its knowledge to act in a way that is expected to achieve its goals in the world • How do you define a search problem? Initial state Successor function Goal test Path cost
Graph Search • In BFS, for example, we shouldn’t bother • expanding the circled nodes (why?)
Costs on Actions • BFS finds the shortest path in terms of number of transitions, not the least-cost path
Priority Queue Refresher • A priority queue is a data structure in which you can insert and retrieve (key, value) pairs with the following operations: • You can promote or demote keys by resetting their • priorities • Unlike a regular queue, insertions into a priority • queue are not constant time, usually O(log n) • We’ll need priority queues for most cost-sensitive • search methods
Uniform Cost Search • What will UCS do for this graph? • What does this mean for completeness?
Uniform Cost Issues • Where will uniform cost explore? • Why? • What is wrong here?
Greedy Best-First Search • Expand the node that seems closest… • What can go wrong?
When should A* terminate? • A* Search orders by the sum: f(n) = g(n) + h(n) • Should we stop when we enqueue a goal? • No! Only stop when we dequeue a goal
Is A* Optimal? • A* Search orders by the sum: f(n) = g(n) + h(n) • What went wrong? • Actual goal cost greater than estimated goal cost • We need estimates to be less than actual costs!
Optimality of A*: Contours • Consider what A* does: • Expands nodes in increasing total f value (fcontours) • Optimal goals have lower f value, so get expanded first
Admissible Heuristics • Most of the work is in coming up with admissible heuristics • Quiz: what’s the simplest admissable heuristic? • Good news: usually admissible heuristics are also consistent • More good news: inadmissible heuristics are still useful effective (Why?)
Relaxed Problems • A version of the problem with fewer restrictions on actions is called a relaxed problem • Relaxed problems of the 8 puzzle: - Each move can swap a tile directly into its final position - Each move can move a tile one step closer to its final position • Relaxed problem for the route planning problem: - You can fly directly to the goal from each state • Relaxed problems for Pac-Man?
8-Puzzle III • How about using the actual cost as a heuristic? - Would it be admissible? - Would we save on nodes? - What’s wrong with it? • With A*, trade-off between quality of estimate and work per node!
Other A* Applications • Robot motion planning • Routing problems • Planning problems • Machine translation • Statistical parsing • Speech recognition
Summary: A* • A* uses both backward costs and (estimates of) forward costs • A* is optimal with admissible heuristics • Heuristic design is key: often use relaxed problems
Local Search Methods • Queue-based algorithms keep fallback options(backtracking) • Local search: improve what you have until youcan’t make it better • Generally much more efficient (but incomplete)
Hill Climbing • Simple, general idea: • Start wherever • Always choose the best neighbor • If no neighbors have better scores than current, quit • Why can this be a terrible idea? • Complete? • Optimal? • What’s good about it?
