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Various Uninform Search Strategies in AI.ppt
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- 2. Today’s class • Goal-basedagents • Representing states and operators • Example problems • Generic state-space search algorithm • Specific algorithms – Breadth-first search – Depth-first search – Uniform cost search – Depth-first iterative deepening • Example problems revisited
- 3. Building goal-based agents Tobuild a goal-based agent we need to answer the following questions: – What is the goal to be achieved? – What are the actions? – What is the representation? • E.g., what relevant information is necessary to encode in order to describe the state of the world, describe the available transitions, and solve the problem?) Initial state Goal state Actions
- 4. What is thegoal to be achieved? • Could describe a situation we want to achieve, a set of properties that we want to hold, etc. • Requires defining a “goal test” so that we know what it means to have achieved/satisfied our goal. • This is a hard question that is rarely tackled in AI, usually assuming that the system designer or user will specify the goal to be achieved. • Certainly psychologists and motivational speakers always stress the importance of people establishing clear goals for themselves as the first step towards solving a problem.
- 5. What are theactions? • Characterize the primitive actions or events that are available for making changes in the world in order to achieve a goal. • Deterministic world: no uncertainty in an action’s effects. Given an action (a.k.a. operator or move) and a description of the current world state, the action completely specifies – whether that action can be applied to the current world (i.e., is it applicable and legal), and – what the exact state of the world will be after the action is performed in the current world (i.e., no need for “history” information to compute what the new world looks like).
- 6. What are theactions? (cont’d) • Note also that actions in this framework can all be considered as discrete events that occur at an instant of time. – For example, if “Mary is in class” and then performs the action “go home,” then in the next situation she is “at home.” There is no representation of a point in time where she is neither in class nor at home (i.e., in the state of “going home”). • The actions are largely problem-specific and determined (intelligently ;-) ) by the system designer. • There usually are multiple action sets for solving the same problem. • Let’s look an example…
- 7. 8-Puzzle Given an initialconfiguration of 8 numbered tiles on a 3 x 3 board, move the tiles in such a way so as to produce a desired goal configuration of the tiles.
- 8. Representing actions • Thenumber of actions / operators depends on the representation used in describing a state. – In the 8-puzzle, we could specify 4 possible moves for each of the 8 tiles, resulting in a total of 4*8=32 operators. – On the other hand, we could specify four moves for the “blank” square and we would only need 4 operators. • Representational shift can greatly simplify a problem!
- 9. Representing states • Whatinformation is necessary to encode about the world to sufficiently describe all relevant aspects to solving the goal? That is, what knowledge needs to be represented in a state description to adequately describe the current state or situation of the world? • The size of a problem is usually described in terms of the number of states that are possible. – The 8-puzzle has 181,440 states. – Tic-Tac-Toe has about 39 states. – Rubik’s Cube has about 1019 states. – Checkers has about 1040 states. – Chess has about 10120 states in a typical game.
- 10. Closed World Assumption •We will generally use the Closed World Assumption. • All necessary information about a problem domain is available in each percept so that each state is a complete description of the world. • There is no incomplete information at any point in time.
- 11. Some example problems •Toy problems and micro-worlds –8-Puzzle –Missionaries and Cannibals –Cryptarithmetic –Remove 5 Sticks –Water Jug Problem • Real-world problems
- 12. 8-Puzzle Given an initialconfiguration of 8 numbered tiles on a 3 x 3 board, move the tiles in such a way so as to produce a desired goal configuration of the tiles.
- 13. • State Representation:3 x 3 array configuration of the tiles on the board. • Operators: Move Blank Square Left, Right, Up or Down. – This is a more efficient encoding of the operators than one in which each of four possible moves for each of the 8 distinct tiles is used. • Initial State: A particular configuration of the board. • Goal: A particular configuration of the board. 8-Puzzle • State Representation: 3 x 3 array configuration of the tiles on the board. • Operators: Move Blank Square Left, Right, Up or Down. – This is a more efficient encoding of the operators than one in which each of four possible moves for each of the 8 distinct tiles is used. • Initial State: A particular configuration of the board. • Goal: A particular configuration of the board.
- 14. The 8-Queens Problem StateRepresentation: ? Initial State: ? Operators: ? Goal: Place eight queens on a chessboard such that no queen attacks any other!
- 15. Missionaries and Cannibals Threemissionaries and three cannibals wish to cross the river. They have a small boat that will carry up to two people. Everyone can navigate the boat. If at any time the Cannibals outnumber the Missionaries on either bank of the river, they will eat the Missionaries. Find the smallest number of crossings that will allow everyone to cross the river safely.
- 16. • Goal: Moveall the missionaries and cannibals across the river. • Constraint: Missionaries can never be outnumbered by cannibals on either side of river, or else the missionaries are killed. • State: configuration of missionaries and cannibals and boat on each side of river. • Initial State: 3 missionaries, 3 cannibals and the boat are on the near bank • Operators: Move boat containing some set of occupants across the river (in either direction) to the other side. Missionaries and Cannibals • Goal: Move all the missionaries and cannibals across the river. • Constraint: Missionaries can never be outnumbered by cannibals on either side of river, or else the missionaries are killed. • State: configuration of missionaries and cannibals and boat on each side of river. • Initial State: 3 missionaries, 3 cannibals and the boat are on the near bank • Operators: Move boat containing some set of occupants across the river (in either direction) to the other side.
- 17. Missionaries and CannibalsSolution Near side Far side 0 Initial setup: MMMCCC B - 1 Two cannibals cross over: MMMC B CC 2 One comes back: MMMCC B C 3 Two cannibals go over again: MMM B CCC 4 One comes back: MMMC B CC 5 Two missionaries cross: MC B MMCC 6 A missionary & cannibal return: MMCC B MC 7 Two missionaries cross again: CC B MMMC 8 A cannibal returns: CCC B MMM 9 Two cannibals cross: C B MMMCC 10 One returns: CC B MMMC 11 And brings over the third: - B MMMCCC
- 18. Cryptarithmetic • Find anassignment of digits (0, ..., 9) to letters so that a given arithmetic expression is true. examples: SEND + MORE = MONEY and FORTY Solution: 29786 + TEN 850 + TEN 850 ----- ----- SIXTY 31486 F=2, O=9, R=7, etc.
- 19. • State: mappingfrom letters to digits • Initial State: empty mapping • Operators: assign a digit to a letter • Goal Test: whether the expression is true given the complete mapping Cryptarithmetic Find an assignment of digits to letters so that a given arithmetic expression is true. examples: SEND + MORE = MONEY and FORTY Solution: 29786 + TEN 850 + TEN 850 ----- ----- SIXTY 31486 F=2, O=9, R=7, etc. • State: mapping from letters to digits • Initial State: empty mapping • Operators: assign a digit to a letter • Goal Test: whether the expression is true given the complete mapping Note: In this problem, the solution is NOT a sequence of actions that transforms the initial state into the goal state; rather, the solution is a goal node that includes an assignment of a digit to each letter in the given problem.
- 20. Remove 5 Sticks Giventhe following configuration of sticks, remove exactly 5 sticks in such a way that the remaining configuration forms exactly 3 squares. • State: ? • Initial State: ? • Operators: ? • Goal Test: ?
- 21. Water Jug Problem Givena full 5-gallon jug and a full 2-gallon jug, fill the 2-gallon jug with exactly one gallon of water. • State: ? • Initial State: ? • Operators: ? • Goal State: ? 5 2
- 22. Water Jug Problem •State = (x,y), where x is the number of gallons of water in the 5-gallon jug and y is # of gallons in the 2-gallon jug • Initial State = (5,2) • Goal State = (*,1), where * means any amount Name Cond. Transition Effect Empty5 – (x,y)→(0,y) Empty 5-gal. jug Empty2 – (x,y)→(x,0) Empty 2-gal. jug 2to5 x ≤ 3 (x,2)→(x+2,0) Pour 2-gal. into 5-gal. 5to2 x ≥ 2 (x,0)→(x-2,2) Pour 5-gal. into 2-gal. 5to2part y < 2 (1,y)→(0,y+1) Pour partial 5-gal. into 2- gal. Operator table 5 2
- 23. Graph Search function graph-search(problem, QUEUEING-FUNCTION) ;; problem describes the start state, operators, goal test, and operator costs ;; queueing-function is a comparator function that ranks two states ;; graph-search returns either a goal node or failure nodes = MAKE-QUEUE(MAKE-NODE(problem.INITIAL-STATE)) closed = {} loop if EMPTY(nodes) then return "failure" node = REMOVE-FRONT(nodes) if problem.GOAL-TEST(node.STATE) succeeds then return node.SOLUTION if node.STATE is not in closed then ADD(node, closed) nodes = QUEUEING-FUNCTION(nodes, EXPAND(node, problem.OPERATORS)) end ;; Note: The goal test is NOT done when nodes are generated ;; Note: closed should be implemented as a hash table for efficiency
- 24. Graph Search Strategies •Breadth-first search and uniform-cost search are optimal graph search strategies. • Iterative deepening search and depth-first search can follow a non-optimal path to the goal. • Iterative deepening search can be used with modification: – It must check whether a new path to a node is better than the original one – If so, IDS must revise the depths and path costs of the node’s descendants.