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Artificial Intelligence

Artificial Intelligence. Problem Solving. Eriq Muhammad Adams eriq.adams@ub.ac.id. Outline. Problem Solving Agents Problem Formulation Example Problems Basic Search Algorithms. sensors. environment. ?. agent. actuators. Problem-Solving Agent. sensors. environment. ?. agent.

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Artificial Intelligence

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  1. Artificial Intelligence Problem Solving Eriq Muhammad Adams eriq.adams@ub.ac.id

  2. Outline • Problem Solving Agents • Problem Formulation • Example Problems • Basic Search Algorithms

  3. sensors environment ? agent actuators Problem-Solving Agent

  4. sensors environment ? agent actuators • Formulate Goal • Formulate Problem • States • Actions • Find Solution Problem-Solving Agent

  5. Problem-solving agents CS 3243 - Blind Search

  6. Example: Route finding

  7. Holiday Planning • On holiday in Romania; Currently in Arad. Flight leaves tomorrow from Bucharest. • Formulate Goal: Be in Bucharest • Formulate Problem: States: various cities Actions: drive between cities • Find solution: Sequence of cities: Arad, Sibiu, Fagaras, Bucharest

  8. States Actions Solution Goal Start Problem Formulation

  9. Vacuum World

  10. Search Problem • State space • each state is an abstract representation of the environment • the state space is discrete • Initial state • Successor function • Goal test • Path cost

  11. Search Problem • State space • Initial state: • usually the current state • sometimes one or several hypothetical states (“what if …”) • Successor function • Goal test • Path cost

  12. Search Problem • State space • Initial state • Successor function: • [state  subset of states] • an abstract representation of the possible actions • Goal test • Path cost

  13. Search Problem • State space • Initial state • Successor function • Goal test: • usually a condition • sometimes the description of a state • Path cost

  14. Search Problem • State space • Initial state • Successor function • Goal test • Path cost: • [path  positive number] • usually, path cost = sum of step costs • e.g., number of moves of the empty tile

  15. 8 2 1 2 3 3 4 7 4 5 6 5 1 6 7 8 Initial state Goal state Example: 8-puzzle

  16. 0.18 sec 6 days 12 billion years 10 million states/sec Example: 8-puzzle Size of the state space = 9!/2 = 181,440 15-puzzle  .65 x1012 24-puzzle  .5 x1025

  17. Example: Robot navigation What is the state space?

  18. Cost of one horizontal/vertical step = 1 Cost of one diagonal step = 2 Example: Robot navigation

  19. Example: Robot navigation

  20. Assumptions in Basic Search • The environment is static • The environment is discretizable • The environment is observable • The actions are deterministic

  21. Simple Agent Algorithm Problem-Solving-Agent • initial-state  sense/read state • goal  select/read goal • successor  select/read action models • problem  (initial-state, goal, successor) • solution  search(problem) • perform(solution)

  22. Search of State Space

  23. Search of State Space

  24. Search of State Space

  25. Search of State Space

  26. Search of State Space

  27. Search of State Space  search tree

  28. Basic Search Concepts • Search tree • Search node • Node expansion • Search strategy: At each stage it determines which node to expand

  29. 8 8 2 2 8 2 7 3 3 4 4 7 7 3 4 5 5 1 1 6 6 5 1 6 8 2 3 4 7 8 4 8 2 2 3 4 7 3 7 5 1 6 5 1 6 5 1 6 Search Nodes  States The search tree may be infinite even when the state space is finite

  30. Implementation: states vs. nodes • A state is a (representation of) a physical configuration • A node is a data structure constituting part of a search tree includes state, parent node, action, path costg(x), depth • The Expand function creates new nodes, filling in the various fields and using the SuccessorFn of the problem to create the corresponding states. CS 3243 - Blind Search

  31. Node Data Structure • STATE • PARENT • ACTION • COST • DEPTH If a state is too large, it may be preferable to only represent the initial state and (re-)generate the other states when needed

  32. Fringe • Set of search nodes that have not been expanded yet • Implemented as a queue FRINGE • INSERT(node,FRINGE) • REMOVE(FRINGE) • The ordering of the nodes in FRINGE defines the search strategy

  33. Search Algorithm • If GOAL?(initial-state) then return initial-state • INSERT(initial-node,FRINGE) • Repeat: • If FRINGE is empty then return failure • n REMOVE(FRINGE) • s  STATE(n) • For every state s’ in SUCCESSORS(s) • Create a node n’ • If GOAL?(s’) then return path or goal state • INSERT(n’,FRINGE)

  34. Search Strategies • A strategy is defined by picking the order of node expansion • Performance Measures: • Completeness – does it always find a solution if one exists? • Time complexity – number of nodes generated/expanded • Space complexity – maximum number of nodes in memory • Optimality – does it always find a least-cost solution • Time and space complexity are measured in terms of • b – maximum branching factor of the search tree • d – depth of the least-cost solution • m – maximum depth of the state space (may be ∞)

  35. Remark • Some problems formulated as search problems are NP-hard (non-deterministic polynomial-timehard) problems. We cannot expect to solve such a problem in less than exponential time in the worst-case • But we can nevertheless strive to solve as many instances of the problem as possible

  36. Blind vs. Heuristic Strategies • Blind (or uninformed) strategies do not exploit any of the information contained in a state (or use only the information available in the problem definition) • Heuristic (or informed) strategies exploits such information to assess that one node is “more promising” than another

  37. Step cost = 1 Step cost = c(action)  > 0 Blind Strategies • Breadth-first • Bidirectional • Depth-first • Depth-limited • Iterative deepening • Uniform-Cost

  38. 1 2 3 4 5 6 7 Breadth-First Strategy New nodes are inserted at the end of FRINGE FRINGE = (1)

  39. 1 2 3 4 5 6 7 Breadth-First Strategy New nodes are inserted at the end of FRINGE FRINGE = (2, 3)

  40. 1 2 3 4 5 6 7 Breadth-First Strategy New nodes are inserted at the end of FRINGE FRINGE = (3, 4, 5)

  41. 1 2 3 4 5 6 7 Breadth-First Strategy New nodes are inserted at the end of FRINGE FRINGE = (4, 5, 6, 7)

  42. Evaluation • b: branching factor • d: depth of shallowest goal node • Complete • Optimal if step cost is 1 • Number of nodes generated:1 + b + b2 + … + bd + b(bd-1) = O(bd+1) • Time and space complexity is O(bd+1)

  43. Time and Memory Requirements Assumptions: b = 10; 1,000,000 nodes/sec; 100bytes/node

  44. Bidirectional Strategy 2 fringe queues: FRINGE1 and FRINGE2 Time and space complexity =O(bd/2)<<O(bd)

  45. 2 3 FRINGE = (1) 4 5 Depth-First Strategy New nodes are inserted at the front of FRINGE 1

  46. 2 3 FRINGE = (2, 3) 4 5 Depth-First Strategy New nodes are inserted at the front of FRINGE 1

  47. 2 3 FRINGE = (4, 5, 3) 4 5 Depth-First Strategy New nodes are inserted at the front of FRINGE 1

  48. 2 3 4 5 Depth-First Strategy New nodes are inserted at the front of FRINGE 1

  49. 2 3 4 5 Depth-First Strategy New nodes are inserted at the front of FRINGE 1

  50. 2 3 4 5 Depth-First Strategy New nodes are inserted at the front of FRINGE 1

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