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Classic AI Search Problems Sliding tile puzzles 8 Puzzle (3 by 3 variation) Small number of 8!/2, about 1.8 *10 5 states 15 Puzzle (4 by 4 variation) Large number of 16!/2 , about 1.0 *10 13 states 24 Puzzle (5 by 5 variation) Huge number of 25!/2 , about 7.8 *10 25 states

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## Classic AI Search Problems

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**Classic AI Search Problems**• Sliding tile puzzles • 8 Puzzle (3 by 3 variation) • Small number of 8!/2, about 1.8 *105 states • 15 Puzzle (4 by 4 variation) • Large number of 16!/2, about 1.0 *1013 states • 24 Puzzle (5 by 5 variation) • Huge number of 25!/2, about 7.8 *1025 states • Rubik’s Cube (and variants) • 3 by 3 by 3 4.3 * 1019 states • Navigation (Map searching)**2**1 3 4 7 6 5 8 Classic AI Search Problems • Invented by Sam Loyd in 1878 • 16!/2, about1013 states • Average number of 53 moves to solve • Known diameter (maximum length of optimal path) of 87 • Branching factor of 2.13**3*3*3 Rubik’s Cube**• Invented by Rubik in 1974 • 4.3 * 1019 states • Average number of 18 moves to solve • Conjectured diameter of 20 • Branching factor of 13.35**Navigation**Arad to Bucharest start end**Arad**Zerind Sibiu Timisoara Oradea Fagaras Arad Rimnicu Vilcea Sibiu Bucharest Representing Search**General (Generic) SearchAlgorithm**function general-search(problem, QUEUEING-FUNCTION) nodes = MAKE-QUEUE(MAKE-NODE(problem.INITIAL-STATE)) loop do if EMPTY(nodes) then return "failure" node = REMOVE-FRONT(nodes) if problem.GOAL-TEST(node.STATE) succeeds then return node nodes = QUEUEING-FUNCTION(nodes, EXPAND(node, problem.OPERATORS)) end A nice fact about this search algorithm is that we can use a single algorithm to do many kinds of search. The only difference is in how the nodes are placed in the queue.**Completeness**solution will be found, if it exists Time complexity number of nodes expanded Space complexity number of nodes in memory Optimality least cost solution will be found Search Terminology**Breadth first**Uniform-cost Depth-first Depth-limited Iterative deepening Bidirectional Uninformed (blind) Search**QUEUING-FN:- successors added to end of queue (FIFO)**Arad Zerind Sibiu Timisoara Oradea Fagaras Arad Rimnicu Vilcea Arad Arad Oradea Lugoj Breadth first**Complete ?**Yes if branching factor (b) finite Time ? 1 + b + b2 + b3 +…+ bd = O(bd), so exponential Space ? O(bd), all nodes are in memory Optimal ? Yes (if cost = 1 per step), not in general Properties of Breadth first**Assuming b = 10, 1 node per ms and 100 bytes per node**Properties of Breadth first cont.**QUEUING-FN:- insert in order of increasing path cost**Arad 75 118 140 Zerind Sibiu Timisoara 140 151 99 80 Oradea Fagaras Arad Rimnicu Vilcea 118 75 71 111 Arad Arad Lugoj Oradea Uniform-cost**Complete ?**Yes if step cost >= epsilon Time ? Number of nodes with cost <= cost of optimal solution Space ? Number of nodes with cost <= cost of optimal solution Optimal ?- Yes Properties of Uniform-cost**QUEUING-FN:- insert successors at front of queue (LIFO)**Arad Zerind Zerind Sibiu Sibiu Timisoara Timisoara Arad Oradea Depth-first**Complete ?**No:- fails in infinite- depth spaces, spaces with loops complete in finite spaces Time ? O(bm), bad if m is larger than d Space ? O(bm), linear in space Optimal ?:- No Properties of Depth-first**Choose a limit to depth first strategy**e.g 19 for the cities Works well if we know what the depth of the solution is Otherwise use Iterative deepening search (IDS) Depth-limited**Complete ?**Yes if limit, l >= depth of solution, d Time ? O(bl) Space ? O(bl) Optimal ? No Properties of depth limited**Iterative deepening search (IDS)**function ITERATIVE-DEEPENING-SEARCH(): for depth = 0 to infinity do if DEPTH-LIMITED-SEARCH(depth) succeeds then return its result end return failure**Complete ?**Yes Time ? (d + 1)b0 + db1 + (d - 1)b2 + .. + bd = O(bd) Space ? O(bd) Optimal ? Yes if step cost = 1 Properties of IDS**Various uninformed search strategies**Iterative deepening is linear in space not much more time than others Use Bi-directional Iterative deepening were possible Summary**Island Search**Suppose that you happen to know that the optimal solution goes thru Rimnicy Vilcea…**Island Search**Suppose that you happen to know that the optimal solution goes thru Rimnicy Vilcea… Rimnicy Vilcea**A* Search**• Uses evaluation function f = g+ h • g is a cost function • Total cost incurred so far from initial state • Used by uniform cost search • h is an admissible heuristic • Guess of the remaining cost to goal state • Used by greedy search • Never overestimating makes h admissible**A***Our Heuristic**QUEUING-FN:- insert in order of f(n) = g(n) + h(n)**Zerind Sibiu Timisoara A* Arad g(Zerind) = 75 g(Timisoara) = 118 g(Sibiu) = 140 g(Timisoara) = 329 h(Zerind) = 374 h(Sibiu) = 253 f(Sibui) = … f(Zerind) = 75 + 374**Properties of A***• Optimal and complete • Admissibility guarantees optimality of A* • Becomes uniform cost search if h= 0 • Reduces time bound from O(b d ) to O(b d - e) • b is asymptotic branching factor of tree • d is average value of depth of search • e is expected value of the heuristic h • Exponential memory usage of O(b d ) • Same as BFS and uniform cost. But an iterative deepening version is possible … IDA***IDA***• Solves problem of A* memory usage • Reduces usage from O(b d ) to O(bd ) • Many more problems now possible • Easier to implement than A* • Don’t need to store previously visited nodes • AI Search problem transformed • Now problem of developing admissible heuristic • Like The Price is Right, the closer a heuristic comes without going over, the better it is • Heuristics with just slightly higher expected values can result in significant performance gains**A* “trick”**• Suppose you have two admissible heuristics… • But h1(n) > h2(n) • You may as well forget h2(n) • Suppose you have two admissible heuristics… • Sometimes h1(n) > h2(n) and sometimes h1(n) < h2(n) • We can now define a better heuristic, h3 • h3(n) = max( h1(n) , h2(n) )**What different does the heuristic make?**• Suppose you have two admissible heuristics… • h1(n) is h(n) = 0 (same as uniform cost) • h2(n) is misplaced tiles • h3(n) is Manhattan distance**Game Search (Adversarial Search)**• The study of games is called game theory • A branch of economics • We’ll consider special kinds of games • Deterministic • Two-player • Zero-sum • Perfect information**Games**• A zero-sum game means that the utility values at the end of the game total to 0 • e.g. +1 for winning, -1 for losing, 0 for tie • Some kinds of games • Chess, checkers, tic-tac-toe, etc.**Problem Formulation**• Initial state • Initial board position, player to move • Operators • Returns list of (move, state) pairs, one per legal move • Terminal test • Determines when the game is over • Utility function • Numeric value for states • E.g. Chess +1, -1, 0**Game Trees**• Each level labeled with player to move • Max if player wants to maximize utility • Min if player wants to minimize utility • Each level represents a ply • Half a turn**Optimal Decisions**• MAX wants to maximize utility, but knows MIN is trying to prevent that • MAX wants a strategy for maximizing utility assuming MIN will do best to minimize MAX’s utility • Consider minimaxvalue of each node • Utility of node assuming players play optimally**Minimax Algorithm**• Calculate minimax value of each node recursively • Depth-first exploration of tree • Game tree (aka minimax tree) Max node Min node**Min**2 7 5 5 6 7 Example Max 5 4 2 5 10 4 6 Utility**Minimax Algorithm**• Time Complexity? • O(bm) • Space Complexity? • O(bm) or O(m) • Is this practical? • Chess, b=35, m=100 (50 moves per player) • 3510010154 nodes to visit**Alpha-Beta Pruning**• Improvement on minimax algorithm • Effectively cut exponent in half • Prune or cut out large parts of the tree • Basic idea • Once you know that a subtree is worse than another option, don’t waste time figuring out exactly how much worse**Alpha-Beta Pruning Example**3 2 3 0 3 30 a pruned 32 a pruned 3 5 2 0 5 3 53 b pruned 3 5 1 2 2 0 2 1 2 3 5 0

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