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Chapter 2

King Saud University College of Computer and Information Sciences Information Technology Department IT422 - Intelligent systems . Chapter 2. Problem Solving by Searching (1). Objectives. Learn how to formulate a search problem.

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Chapter 2

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  1. King Saud University College of Computer and Information Sciences Information Technology Department IT422 - Intelligent systems Chapter 2 Problem Solving by Searching (1)

  2. Objectives • Learn how to formulate a search problem. • Learn the different algorithms used to solve problems by searching. • Learn how to assess the performance of a search algorithm.

  3. Introduction • Solving a particular problem → Need to define the elements of a problem and its solution. • Before start searching for solutions, Identify the goal and Define the problem precisely. • Isolate and represent the task knowledge that is necessary to solve the problem. • Choose and apply the best problem solving technique(s) to the particular problem.

  4. Introduction: Problem solving as search Task = Search • Search is required to solve a wide range of problems. • Search is a method that can be used by computers to examine a huge problem space to find a goal. • e.g Searching for a contact lens on a football field • Challenge: How to find the goal as quickly as possible or without using too many resources.

  5. Introduction: Problem solving as search • A problem can be considered to consist of a goal and a set of actions that can be taken to lead to the goal • Problem: On holiday in Romania; currently in Arad. Flight leaves tomorrow from Bucharest. Find a short route to drive to Bucharest. • Task = finding the sequence of actions that lead to the desirable goal. Start End

  6. Introduction: Problem solving as search • Toy problems • Vacuum world • The N-Queen • Rubik's cube • The 8-Puzzle • The 8-puzzle belongs to the family of Sliding-block puzzles. • The 8-puzzle has 9!/2=181,440 reachable states. • The 15-puzzle has around 1.3 trillion states… • Real world Problems • Touring problems • - Route Finding • - Travelling salesperson • - VLSI Layout • - Robot navigation • - Internet searching

  7. Introduction: Problem solving as search • Search can be performed without any additional information about how to reach a solution: Blind Search (Uniformed search) • Search can be performed using heuristics: Informed Search

  8. Uninformed search • To successfully operate, blind search should satisfy some properties: • It must be complete: It must generate every possible solution otherwise it might miss a suitable solution. • It must be able to find the best solution.

  9. Uninformed search • Key questions to be addressed: • What goal do we need to achieve? • How to know when a goal is reached? • What knowledge we need? • What actions do we need to do? • A goal can be described as: • A task to be accomplished • A situation to be reached • A set of properties to be acquired

  10. Uninformed search: Basic concepts • State: configuration, situation, point…… • Problem formulation: Process of deciding what actions and states to consider given a goal. • Solution: Sequence of actions that help achieving the goal: a path from an initial state to the goal state . • Search: Process of looking for a solution. Takes input (problem) and returns solution. • Execution: To carry out the actions recommended by the solution

  11. Uninformed search: Problem formulation • Problem formulation: a problem can be formally defined by: • State representation: What information is necessary to encode to solve a problem. • Initial state: starting point. • Actions: A description of the possible actions that can be executed in a particular state. • Transition model (Description of what each action does) • Goal test: determines whether a given state is a goal state. • Path cost function: assigns a cost to each path. Should reflect the performance measure used to assess the quality of any solution.

  12. Uninformed search: Problem formulation • Successor function: • Given a particular state x, fsuccessor(x) = {(<action, new state>) } where action is one of the legal actions in state x and new state is the successor state that can be reached from x by applying action. • The successor function allows together with the initial state to define the state space. • STATE SPACE: The set of allstates reachable from the initial state. (see AIMA 3rd Ed. page 67) • A path in the state space is a sequence of states connected by a sequence of actions.

  13. Uninformed search: Problem formulation • More about state space: • A state space can be represented as a directed graph: • where the nodes are states and the arcs between them are the actions • e.g.: theMap of Romaniain slide #5 (if we view each road as standing for two driving directions)

  14. Uninformed search: Problem formulation Example problem 1: The vacuum world • State representation: The agent is on one of two locations, each of which might or might not contain dirt. Thus, there are 2 x 22 = 8 possible world states. • Initial state: Any state can be designated as the initial state. • Actions: each state has just 3 actions: Left, Right, and Suck. • Transition Model: Generates the legal states that result from applying the following actions: (Left, Right, Suck). • Goal test: Check whether all the squares are clean. • Path cost function: Each step costs 1. So the path cost is the number of steps.

  15. Uninformed search: Problem formulation Example problem 1: The vacuum world state space as a graph

  16. Uninformed search: Problem formulation • If the initial state is known, for simplicity, the state space can also be drawn as a tree. • We designate an initial state for the vacuum world:

  17. Uninformed search: Problem formulation • A tree is a directed acyclic graph all of whose nodes have at most one parent. • A root of a tree is a node with no parents. • A leaf is a node with no children. • Graphs can be turned into trees by duplicating nodes and breaking cyclic paths, if any. • To convert a graph into a tree: • choose a root node • trace every path from that node until you reach a leaf node (goal) or a node already in that path (Repeated State or R.S.) • AGAIN, trees are used only for simplicity, i.e. if the problem is complex and it will be hard to draw it as a graph.

  18. Uninformed search: Problem formulation • Vacuum world state space as a tree L S R R.S. L L R S R S R.S. R.S. L R.S. R.S. L R S R S R.S. R.S. R.S. R.S. Goal State L R S What is the total number of unrepeated states? R.S. R.S. Goal State

  19. Uninformed search: Problem formulation • Solution definition: • A potential solution is a path from the initial state to a goal state. • Solution quality is measured by the path cost function. • An optimal solution has the lowest path cost among all solutions.

  20. Arad Zerind Sibiu Timisoara Oradea Fagaras Arad Rimnicu Vilcea Sibiu Bucharest Uninformed search: Problem formulation • An example of solution path:

  21. Uninformed search: Problem formulation Example problem 2 - The Touring problem: Given a set of n cities, the touring problem consists in visiting cities at least once starting and ending in the same city. • States: Specified by the current city and the set of cities already visited. • Initial state: Any state can be designed as the initial state. • Actions: take a trip between adjacent cities. • Transition model (Successor function): Generates the next city to visit according to the current state. • Goal test: Ending city reached and all cities have been visited. • Path cost: Sum of all step costs.

  22. Uninformed search: Searching for solutions • To solve the problem, we need to define a search strategy that allows to explore efficiently the state space. • A solution is an action sequence. • The possible action sequences starting at the initial state form a search tree. • Each state is represented by a node. • Expansionoperation: A node is expanded if the successor function for the corresponding state generates new states.

  23. Uninformed search: Informal description

  24. Uninformed search: Towards a formal description • Search algorithms require a data structure to keep track of the search tree. • Node n definition: Data structure with 5 components: • State: representation of a physical configuration. • Parent node: The node in the search tree that generated this node. • Action: action applied to the parent node to generate the node. • Path cost: cost denoted by g(n), of the path from the initial state to the node n. • Depth: The number of steps along the path from the initial state. Parent node Node (State) Children

  25. Uninformed search: Towards a formal description • How to deal with non expanded nodes? Frontier: set of nodes generated but not yet expanded. Each node is a leaf node. • What is the suitable representation of frontier? • Set of nodes: Simplest way but could be computationally expensive. • Queue: best implementation of the frontier.

  26. Uninformed search: Towards a formal description • What are the operations to be performed on the queue? • Make-Node(state,parent,action,depth,cost): creates a node with the given parameters. • Empty?(queue): returns TRUE only if there are no more nodes in the queue. • First(queue) : returns the first node of the queue. • Remove-First(queue): returns First(queue) and removes it from the queue. • Insert(element, queue): inserts a node into the queue and returns the resulting queue. • Insert-All(elements, queue): inserts a set of nodes into the queue and returns the resulting queue.

  27. Uninformed search: Repeated states • Problem: Possibility of wasting time by expanding states that have already been encountered and expanded before. • Occurs when actions are reversible. (Example: move left, and move right) • A solvable problem can become unsolvable if the algorithm does not detect them systematically. • For detection purposes, a comparison operation is needed. Solution: keep a list to store every expanded node.

  28. Uninformed search: Avoiding repeated states

  29. Uninformed search: Performance evaluation • The performance of problem solving algorithms can be evaluated along the following dimensions: • Completeness: is it guaranteed to find a solution when there is one? • Optimality: Does the strategy find the optimal solution (i.e., Has the lowest path cost among all solutions)? • Time complexity: how long does it take to find a solution? It can be measured by the number of generated nodes. • Space complexity: how much memory is needed to perform the search? It can be measured in terms of the maximum number of nodes stored in memory.

  30. Uninformed search: Performance evaluation Complexity for search on tree is expressed in terms of 3 quantities : • b: The branching factor: maximum number of successors of any node. • d: The depth of the shallowest goal node (i.e., the number of steps along the path from the root). • m: The maximum length of any path in the state space.

  31. Uninformed search: Basic strategies • Breadth First Search (BFS) • Depth First Search (DFS) • Depth Limited Search (DLS) • Uniform Cost Search (UCS) • Iterative Deepening Search (IDS)

  32. Uninformed search: Basic strategies • Strategies that order nodes without using any domain specific information (only problem definition is provided). • Generate successors and simply differentiate between a goal state and a non goal state. • Blind search strategies differ by the order of node expansion.

  33. Uninformed search: Breadth First Search • Main idea: Nodes at depth i are expanded before nodes at depth (i+1). • Implementation: use of a First-In-First-Out queue (FIFO) for the frontier. Nodes visited first are expanded first. • The goal test is applied to each node when it is generated rather then when it is selected for expansion. • Shallow nodes are expanded before deeper nodes.

  34. Breadth-first search on a graph function BREADTH-FIRST-SEARCH(problem) returns a solution, or failure node ←a node with STATE = problem.INITIAL-STATE, PATH-COST = 0 ifproblem.GOAL-TEST(node.STATE) thenreturn SOLUTION(node) frontier ←a FIFO queue with node as the only element explored ←an empty set loop do if EMPTY?( frontier) then return failure node←POP( frontier ) /* chooses the shallowest node in frontier */ add node.STATE to explored for each action inproblem.ACTIONS(node.STATE) do child ←CHILD-NODE(problem, node, action) if child .STATE is not in explored or frontier then ifproblem.GOAL-TEST(child .STATE) thenreturn SOLUTION(child ) frontier ←INSERT(child , frontier )

  35. Uninformed search: BFS evaluation • Completeness: Yes, if the branching factor b is finite. • Optimality: shallowest goal is not necessarily the optimal one. It is optimal if all actions have the same cost. • Time complexity: At the worst case, BFS expands every node (except goal node) thus taking time as follows: 1+b+ + b2 + b3 +….+ bd = O(bd) If the goal test is applied to nodes when selected for expansion, rather then generated, the time complexity would be: O(bd+1) • Memory complexity: BFS keeps every node in memory. Space is a big problem.

  36. Uninformed search: Depth First Search • DFS expands the deepest node in the current frontier on the search tree. • DFS algorithm is an instance of the graph-search algorithm. • As nodes are expanded, they are dropped from the frontier so then the search “backs up” to the next shallowest node that still has unexplored successors. • DFS strategy is implemented using (LIFO) queue or stack (i.e., the most recently generated node is chosen for expansion). • Another alternative is to implement DFS with a recursive function that calls itself on each of its children in turn.

  37. Uninformed search: Depth First Search

  38. Uninformed search: DFS evaluation • Completeness: Incomplete in case of unbounded depth containing no solution. • Optimality: does not provide always optimal solutions. • Time complexity: At the worst case, DFS generates about O(bm) nodes in the search tree. • Memory complexity: DFS requires less memory than BFS. It needs to store only a single path from the root to a leaf node along with the remaining unexpanded sibling nodes for each node in the path. The required storage is O(bm) where b is the branching factor and m maximum depth.

  39. Uninformed search: Depth Limited Search • It is simply DFS with a depth bound. • Searching is not permitted beyond the depth bound. • Works well if we know what the depth of the solution is. • Termination is guaranteed. • If the solution is beneath the depth bound, the search cannot find the goal (hence this search algorithm is incomplete). • Otherwise use Iterative deepening search (IDS).

  40. Uninformed search: Iterative Deepening depth First Search

  41. Uninformed search: IDS (cont.) • Key idea: Iterative deepening search (IDS) applies DLS repeatedly with increasing depth. It terminates when a solution is found or no solutions exists. • IDS combines the benefits of BFS and DFS: Like DFS the memory requirements are very modest (O(bd)). Like BFS, it is complete when the branching factor is finite. IDS is optimal when the step costs are all identical. • The time complexity: the total number of generated nodes is : • N(IDS)=(d)b + (d-1) b2 + …+(1)bd=O(bd) • In general, iterative deepening is the preferred Uninformed search method when there is a large search space and the depth of the solution is not known.

  42. Uninformed search: Uniform Cost Search • In case of equal step costs, Breadth First search finds the optimal solution. • For any step-cost function, Uniform Cost search expands the node n with the lowest path cost. • UCS takes into account the total cost. • UCS is guided by path costs rather than depths. Nodes are ordered according to their path cost.

  43. UCS algorithm on a graph

  44. Uninformed search: UCS

  45. Uninformed search: UCS

  46. Uninformed search: UCS

  47. Bidirectional Search • Idea: run 2 simultaneous searches. One forward from the initial state and the other backward from the goal. The hope target is that the 2 searches meet in the middle. • Motivation: bd/2+ bd/2 < bd • Implementation: check whether the frontiers of the 2 searches intersect rather than the goal test. If they do, a solution has been found (not necessarily an optimal one).

  48. Bidirectional Search • The check is done when each node is generated or selected for expansion and, with a hash table, will take constant time. • Example: A problem has solution depth d=6 • Each direction runs BFS one node at a time. In the worst case they will meet when they have generated all of the nodes at depth 3. • For b=10, total node generations=2220 compared with 1 111 110 for a standard BFS. • Weakness: At least 1 of the 2 frontiers must be kept in memory. • Bidirectional search is more attractive because the reduction of time complexity is significant. But how do we search backward? Some cases may require substantial ingenuity.

  49. Uninformed search: Summary • Search: process of constructing sequences of actions that achieve a goal given a problem. • Goal identification is the first step in solving problems by searching. It facilitates problem formulation. • Formulating a problem requires specifying 5 components: Initial state, a set of actions, a transition model describing the results of those actions, a goal test and path cost function. Environment is represented as a state space. • A solution is a path from the initial state to a goal state. • Search algorithms are judged on the basis of completeness, optimality, time complexity and space complexity. • Several search strategies, the basic algorithms are: BFS, DFS, DLS, IDS,UCS and Bidirectional search.

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