Download to read offline
![Example: Towers of Hanoi*
* A complete direct graph representation
[http://en.wikipedia.org/wiki/Tower_of_Hanoi]](https://image.slidesharecdn.com/uninformed-and-informed-search-250908041058-78d02e12/85/UNINFORMED-AND-INFORMED-SEARCH-ALGORITHMS-10-320.jpg)
![Example: Towers of Hanoi*
* A complete direct graph representation
[http://en.wikipedia.org/wiki/Tower_of_Hanoi]](https://image.slidesharecdn.com/uninformed-and-informed-search-250908041058-78d02e12/75/UNINFORMED-AND-INFORMED-SEARCH-ALGORITHMS-10-2048.jpg)
UNINFORMED AND INFORMED SEARCH ALGORITHMS
- 1. LECTURE 1 INTELLIGENT SYSTEMS(CSE-303-F) Section A UNINFORMED AND INFORMED SEARCH
- 2. Outline Motivation TechnicalSolution Uninformed Search Depth-First Search Breadth-First Search Informed Search Best-First Search Hill Climbing A* Illustration by a Larger Example Extensions Summary
- 3. Motivation One ofthe major goals of AI is to help humans in solving complex tasks How can I fill my container with pallets? Which is the shortest way from Milan to Innsbruck? Which is the fastest way from Milan to Innsbruck? How can I optimize the load of my freight to maximize my revenue? How can I solve my Sudoku game? What is the sequence of actions I should apply to win a game? Sometimes finding a solution is not enough, you want the optimal solution according to some “cost” criteria All the example presented above involve looking for a plan A plan that can be defined as the set of operations to be performed of an initial state, to reach a final state that is considered the goal state Thus we need efficient techniques to search for paths, or sequences of actions, that can enable us to reach the goal state, i.e. to find a plan Such techniques are commonly called Search Methods
- 4. Examples of Problems:Towers of Hanoi 3 pegs A, B, C 3 discs represented as natural numbers (1, 2, 3) which correspond to the size of the discs The three discs can be arbitrarily distributed over the three pegs, such that the following constraint holds: di is on top of dj → di < dj Initial status: ((123)()()) Goal status: (()()(123)) 3 2 1 A B C Operators: Move disk to peg Applying: Move 1 to C (1 → C) to the initial state ((123)()()) a new state is reached ((23)()(1)) Cycles may appear in the solution!
- 5. Examples of Problems: Blocksworld Objects: blocks Attributes (1-ary relations): cleartop(x), ontable(x) Relations: on(x,y) Operators: puttable(x) where x must be cleartop; put(x,y), where x and y must be cleartop E A B C D Goal State E A B C D Initial State • Initial state: – ontable(E), cleartop(E) – ontable(A), cleartop(A) – ontable(B), cleartop(B) – ontable(C) – on(D,C), cleartop (D) • Applying the move put(E,A): – on(E,A), cleartop(E) – ontable(A) – ontable(B), cleartop(B) – ontable(C) – on(D,C), cleartop (D)
- 6.
- 7. Search Space Representation Representing the search space is the first step to enable the problem resolution Search space is mostly represented through graphs A graph is a finite set of nodes that are connected by arcs A loop may exist in a graph, where an arc lead back to the original node In general, such a graph is not explicitly given Search space is constructed during search Loop Node Arc
- 8. Search Space Representation A graph is undirected if arcs do not imply a direction, direct otherwise A graph is connected if every pair of nodes is connected by a path A connected graph with no loop is called tree A weighted graph, is a graph for which a value is associated to each arc undirect direct connected disconnected tree weighted 1 2 4 1 5 6 2 1 1
- 9. Example: Towers ofHanoi* 3 2 1 A B C 3 2 A B C 3 2 A B C 1 1 * A partial tree search space representation 3 A B C 1 2 3 2 A B C 1 … 3 A B C 1 2 A B C 1 2 3 3 A B C 1 2 … … 3 A B C 1 2 … 3 A B C 1 2 3 A B C 1 2 … … … … … … These nodes are equals
- 10. Example: Towers ofHanoi* * A complete direct graph representation [http://en.wikipedia.org/wiki/Tower_of_Hanoi]
- 11. Search Methods Asearch method is defined by picking the order of node expansion Strategies are evaluated along the following dimensions: completeness: does it always find a solution if one exists? time complexity: number of nodes generated space complexity: maximum number of nodes in memory optimality: does it always find the shortest path solution? Time and space complexity are measured in terms of b: maximum branching factor of the search tree d: depth of the shortest path solution m: maximum depth of the state space (may be ∞)
- 12. Search Methods Uninformedtechniques Systematically search complete graph, unguided Also known as brute force, naïve, or blind Informed methods Use problem specific information to guide search in promising directions
- 13. Brute force approachto explore search space UNINFORMED SEARCH
- 14. Uninformed Search Aclass of general purpose algorithms that operates in a brute force way The search space is explored without leveraging on any information on the problem Also called blind search, or naïve search Since the methods are generic they are intrinsically inefficient E.g. Random Search This method selects randomly a new state from the current one If the goal state is reached, the search terminates Otherwise the methods randomly select an other operator to move to the next state Prominent methods: Depth-First Search Breadth-First Search Uniform-Cost Search
- 15. Depth-First Search Depth-FirstSearch (DFS) begins at the root node and exhaustively search each branch to it maximum depth till a solution is found The successor node is selected going in depth using from right to left (w.r.t. graph representing the search space) If greatest depth is reach with not solution, we backtrack till we find an unexplored branch to follow DFS is not complete If cycles are presented in the graph, DFS will follow these cycles indefinitively If there are no cycles, the algorithm is complete Cycles effects can be limited by imposing a maximal depth of search (still the algorithm is incomplete) DFS is not optimal The first solution is found and not the shortest path to a solution The algorithm can be implemented with a Last In First Out (LIFO) stack or recursion
- 16. Depth-First Search: Algorithm Listopen, closed, successors={}; Node root_node, current_node; insert-first(root_node,open) while not-empty(open); current_node= remove-first(open); insert-first (current_node,closed); if (goal(current_node)) return current_node; else successors=successorsOf(current_node); for(x in successors) if(not-in(x,closed)) insert-first(x,open); endIf endWhile N.B.= this version is not saving the path for simplicity
- 17. Depth-First Search: Example S AB S B S A D F F 1 2 3 4 5 6 A C D F open={S} closed ={} 0. Visit S: open={A,B}, closed={S} 1.Visit A: open={S,B,F,B}, closed={A,S} 2.Visit S: open={B,F,B}, closed={S,A,S} 3.Visit B: open={S,A,F,D,F,B}, closed={B,S,A,S} 4.Visit S: open={A,F,D,F,B}, closed={S,B,S,A,S} 5.Visit A: open={F,D,F,B}, closed={A,S,B,S,A,S} 6.Visit F: GOAL Reached!
- 18. Depth-First Search: Example S AB S B S A D F F Result is: S->A->B->F A C D F
- 19. Depth-First Search: Complexity Time Complexity assume (worst case) that there is 1 goal leaf at the RHS so DFS will expand all nodes =1 + b + b2+ ......... + bm = O (bm) where m is the max depth of the tree Space Complexity how many nodes can be in the queue (worst-case)? at each depth l < d we have b-1 nodes at depth m we have b nodes total = (d-1)*(b-1) + b = O(bm) d=0 d=1 m=d=2 G d=0 d=1 d=2 d=3 m=d=4
- 20. Breadth-First Search Breadth-FirstSearch (BFS) begins at the root node and explore level-wise al the branches BFS is complete If there is a solution, BFS will found it BFS is optimal The solution found is guaranteed to be the shortest path possible The algorithm can be implemented with a First In First Out (FIFO) stack
- 21. Breadth-First Search: Algorithm Listopen, closed, successors={}; Node root_node, current_node; insert-last(root_node,open) while not-empty(open); current_node=remove-first(open); insert-last(current_node,closed); if (goal(current_node)) return current_node; else successors=successorsOf(current_node); for(x in successors) if(not-in(x,closed)) insert-last(x,open); endIf endWhile N.B.= this version is not saving the path for simplicity
- 22. Breadth-First Search: Example S AB S B S A D F F 1 2 3 4 5 open = {S}, closed={} 0. Visit S: open = {A,B}, closed={S} 1. Visit A: open={B,S,B,F}, closed={S,A} 2. Visit B: open={S,B,F,F,A,C,D}, closed={S,A,B} 3. Visit S: open={B,F,F,A,C,D}, closed={S,A,B,S} 4. Visit B: open={F,F,A,C,D,S,A,C,D}, closed={S,A,B,S,B} 5. Visit F: Goal Found! A C D F
- 23. Breadth-First Search: Example S AB S B S A D F F Result is: S->A->F A C D F
- 24. Breadth-First Search: Complexity Time complexity is the same magnitude as DFS O (bm) where m is the depth of the solution Space Complexity how many nodes can be in the queue (worst-case)? assume (worst case) that there is 1 goal leaf at the RHS so BFS will store all nodes =1 + b + b2+ ......... + bm = O (bm) 1 3 7 15 14 13 12 11 10 9 8 4 5 6 2 d=0 d=1 d=2 d=3 d=4 G
- 25. Further Uninformed Search Strategies Depth-limited search (DLS): Impose a cut-off (e.g. n for searching a path of length n-1), expand nodes with max. depth first until cut-off depth is reached (LIFO strategy, since it is a variation of depth-first search). Bidirectional search (BIDI): forward search from initial state & backward search from goal state, stop when the two searches meet. Average effort O(bd/2) if testing whether the search fronts intersect has constant effort In AI, the problem graph is typically not known. If the graph is known, to find all optimal paths in a graph with labelled arcs, standard graph algorithms can be used
- 26. Using knowledge onthe search space to reduce search costs INFORMED SEARCH
- 27. Informed Search Blindsearch methods take O(bm) in the worst case May make blind search algorithms prohibitively slow where d is large How can we reduce the running time? Use problem-specific knowledge to pick which states are better candidates
- 28. Informed Search Alsocalled heuristic search In a heuristic search each state is assigned a “heuristic value” (h-value) that the search uses in selecting the “best” next step A heuristic is an operationally-effective nugget of information on how to direct search in a problem space Heuristics are only approximately correct
- 29. Informed Search: Prominent methods Best-First Search A* Hill Climbing
- 30. Cost and CostEstimation f(n)=g(n)+h(n) g(n) the cost (so far) to reach the node n h(n) estimated cost to get from the node to the goal f(n) estimated total cost of path through n to goal
- 31. Informed Search: Best-FirstSearch Special case of breadth-first search Uses h(n) = heuristic function as its evaluation function Ignores cost so far to get to that node (g(n)) Expand the node that appears closest to goal Best First Search is complete Best First Search is not optimal A solution can be found in a longer path (higher h(n) with a lower g(n) value) Special cases: uniform cost search: f(n) = g(n) = path to n A* search 31
- 32. Best-First Search: Algorithm Listopen, closed, successors={}; Node root_node, current_node; insert-last(root_node,open) while not-empty(open); current_node=remove-first (open); insert-last(current_node,closed); if (goal(current_node)) return current_node; else successors=estimationOrderedSuccessorsOf(current_node); for(x in successors) if(not-in(x,closed)) insert-last(x,open); endIf endWhile 32 N.B.= this version is not saving the path for simplicity returns the list of direct descendants of the current node in shortest cost order
- 33. Best-First Search: Example 33 S AB S B S A D F F 1 2 3 open = {S}, closed={} 0. Visit S: open = {A,B}, closed={S} 1. Visit A: open={B,F,B,S}, closed={S,A} 2. Visit B: open={F,B,S,F,A,C,D}, closed={S,A,B} 3. Visit F: Goal Found! A C D F h=1 h=1 h=2 h=2 h=2 h=2 h=2 h=2 h=2 In this case we estimate the cost as the distance from the root node (in term of nodes)
- 34. Best-First Search: Example 34 S AB S B S A D F F A C D F Result is: S->A->F! If we consider real costs, optimal solution is: S->B->F h=1, w=2 h=1, w=1 h=2, w=4 h=2 h=2 h=2 h=2, w=7 h=2 h=2
- 35. A* Derived fromBest-First Search Uses both g(n) and h(n) A* is optimal A* is complete
- 36. A* : Algorithm Listopen, closed, successors={}; Node root_node, current_node, goal; insert-back(root_node,open) while not-empty(open); current_node=remove-front(open); insert-back(current_node,closed); if (current_node==goal) return current_node; else successors=totalEstOrderedSuccessorsOf(current_node); for(x in successors) if(not-in(x,closed)) insert-back(x,open); endIf endWhile 36 N.B.= this version is not saving the path for simplicity returns the list of direct descendants of the current node in shortest total estimation order
- 37. A* : Example 37 S AB S B S A D F F 1 2 3 open = {S}, closed={} 0. Visit S: open = {B,A}, closed={S} 1. Visit B: open={A,C,A,F,D}, closed={S,B} 2. Visit A: open={C,A,F,D,B,S,F}, closed={S,B,A} 3. Visit C: open={A,F,D,B,S,F}, closed={S,B,A,C} 4. Visit A: open={F,D,B,S,F}, closed={S,B,A,C,A} 5. Visit F: Goal Found! A C D F h=2, w=1 g=3 h=2, w=2, g=4 In this case we estimate the cost as the distance from the root node (in term of nodes) h=1, w=2, g=2 h=1, w=1, g=1 h=2, w=4, g=5 h=2, w=7, g=9 h=2, w=1 g=2 h=2 w=3 g=4 h=2, w=4, g=5 4 5
- 38. A* : Example 38 S AB S B S A D F F A C D F h=2, w=1 g=3 h=2, w=2, g=4 h=1, w=2, g=2 h=1, w=1, g=1 h=2, w=4, g=5 h=2, w=7, g=9 h=2, w=1 g=2 h=2 w=3 g=4 h=2, w=4, g=5 Result is: S->B->F!
- 39. Hill Climbing Specialcase of depth-first search Uses h(n) = heuristic function as its evaluation function Ignores cost so far to get to that node (g(n)) Expand the node that appears closest to goal Hill Climbing is not complete Unless we introduce backtracking Hill Climbing is not optimal Solution found is a local optimum
- 40. Hill Climbing: Algorithm Listsuccessors={}; Node root_node, current_node, nextNode; current_node=root_node while (current_node!=null) if (goal(current_node)) return current_node; else successors=successorsOf(current_node); nextEval = -∞; nextNode=null; for(x in successors) if(eval(x)> nextEval) nexEval=eval(x); nextNode=x; current_node=nextNode, endIf endWhile 40 N.B.= this version is not using backtracking
- 41. Hill Climbing: Example 41 S AB S B S A D F F 1 2 3 0. current_node=S, successors (A=1,B=1) 1. current_node=A, successors (B=2,F=2,S=2) 2. current_node=B, successors (F=1,….) 3. current_node=F: Goal Found! A C D F h=1 h=1 h=2 h=2 h=2 h=2 h=2 h=2 h=3 In this case we estimate the cost as the distance from the root node (in term of nodes) h=1
- 42. Hill Climbing: Example 42 S AB S B S A D F F A C D F h=1 h=1 h=2 h=2 h=2 h=2 h=2 h=2 h=3 h=1 Result is: S->A->B->F! Not optimal, more if at step 1 h(S)=2 we would have completed without funding a solution
- 43. Informed Search Algorithm Comparison AlgorithmTime Space Optimal Complete Derivative Best First Search O(bm) O(bm) No Yes BFS Hill Climbing O() O(b) No No A* O(2N) O(bd) Yes Yes Best First Search 43 b, branching factor d, tree depth of the solution m, maximum tree depth
- 44. ILLUSTRATION BY ALARGER EXAMPLE
- 45. Route Search Startpoint: Milan End point: Innsbruck Search space: Cities Nodes: Cities Arcs: Roads Let’s find a possible route!
- 46. Graph Representation Westart from the root node, and pick the leaves The same apply to each leaves But we do not reconsider already used arcs The first node picked is the first node on the right Innsbruck (Goal) Milan (Root) Piacenza Verona Bolzano Trento Merano Landeck Sondrio Bergamo Brescia Lecco Como Lugano Chiavenna Feldkirck 20
- 47. Depth-First Search Innsbruck Milan Piacenza Verona Bolzano Trento Merano Landeck Brescia Lecco Como Bergamo Trento Innsbruck Innsbruck Bolzano Merano Landeck Innsbruck Innsbruck Verona Bolzano Trento Merano Landeck Brescia Trento Innsbruck Innsbruck Bolzano Merano Landeck Innsbruck N.B.:by building the tree, we are exploring the search space! 1 2 3 4 5 According to Google Maps: 464 km – 4 hours 37 mins
- 48. Breadth-First search Innsbruck Milan Piacenza Verona Bolzano Trento Merano Landeck Sondrio Brescia Lecco Como Lugano Chiavenna Feld. Bergamo Trento Innsbruck Innsbruck Bolzano Innsbruck Verona Bolzano Trento Merano Landeck Brescia Trento Innsbruck Innsbruck Bolzano N.B.:by building the tree, we are exploring the search space! 1 2 3 4 5 6 7 8 9 Lecco 10 11 12 13 14 Landeck Innsbruck 15 Lugano 16 Merano 17 20 21 Sondrio Chi. Chi. 18 22 19 23 24 25 26 According to Google Maps: 358 km – 5 hours 18 mins
- 49. Depth-First Search vsBreadth- First search Distance DFS: 464 km BFS: 358 km Q1: Can we use an algorithm to optimize according to distance? Time DFS: 4 hours 37 mins BFS: 5 hours 18 mins Q2: Can we use an algorithm to optimize according to time? Search space: DFS: 5 expansions BFS: 26 expansions Not very relevant… depends a lot on how you pick the order of node expansion, never the less BFS is usually more expensive To solve Q1 and Q2 we can apply for example and Best-First Search Q1: the heuristic maybe the air distance between cities Q2: the heuristic maybe the air distance between cities x average speed (e.g. 90km/h)
- 50. Graph Representation with approximatedistance Innsbruck (Goal) Milan (Root) Piacenza Verona Bolzano Trento Merano Landeck Sondrio Bergamo Brescia Lecco Como Lugano Chiavenna Feldkirck 60 50 45 55 20 180 40 140 63 42 135 100 25 90 55 90 80 60 70 45 70 25 60
- 51. Best-First search Innsbruck Milan Piacenza Verona Bolzano Trento Merano Landeck Sondrio Brescia Lecco Como Lugano Chiavenna Feld. Bergamo Trento Innsbruck Innsbruck Bolzano Innsbruck Verona Bolzano Trento Merano Landeck Brescia Trento Innsbruck Innsbruck Bolzano N.B.:by building the tree, we are exploring the search space! 1 Lecco Landeck Innsbruck Lugano Merano Sondrio Chi. Chi. According to Google Maps: 358 km – 5 hours 18 mins And this is really the shortest way! H=60 H=55 H=50 H=45 H=65 H=70 H=130 H=100 H=110 H=92 4 2 3 5 6 7 8 10 11 H=190 H=220 H=275 H=270 H=160 H=190 H=240 H=245 H=250 H=313 H=150 H=227 H=105 H=245 H=130 H=142 9 12 13 14 15 16 17 18 19 21 20 21 22 29
- 52.
- 53. Variants to presentedalgorithms Combine Depth First Search and Breadth First Search, by performing Depth Limited Search with increased depths until a goal is found Enrich Hill Climbing with random restart to hinder the local maximum and foothill problems Stochastic Beam Search: select w nodes randomly; nodes with higher values have a higher probability of selection Genetic Algorithms: generate nodes like in stochastic beam search, but from two parents rather than from one
- 54.
- 55. Summary Uninformed Search If the branching factor is small, BFS is the best solution If the tree is depth IDS is a good choice Informed Search Heuristic function selection determines the efficiency of the algorithm If actual cost is very expensive to be computed, then Best First Search is a good solution Hill climbing tends to stack in local optimal solutions