Coursehero >> North Carolina >> Wake Forest >> CSC 371

Course Hero has millions of student submitted documents similar to the one below including study guides, homework solutions, papers, exam answer keys and textbook solutions.

Sunday, C:\Userdata\www-home\csc371-671\ilp\L5_notes.txt January 25, 2009 8:47 PM Monday, January 26, 2009 Let's look at the Sudoku puzzle: fully observable; deterministic; sequential; static; discrete; single States: some collection of digits 1-9 in a 9x9 grid Initial State: digits in a 9x9 grid (assume legal and solvable) Successor Function: some way to add a digit to the grid in a legal way Goal test: each sub-square is legal, each column is legal, each row is legal Number of legal states is between 10^20 and 10^21: 6,670,903,752,021,072,936,960 = 2^15 * 3^8 * 5*7 * 27,704,267,971 = 9! * 72^2 * 2^7 * 27,704,267,971 How do we search for solutions using uninformed/blind search? That is, we have no information about the desirability of states other than whether they are legal or not. Random search Fill in every empty cell in every sub-square with random values 1-9 that are not already present. Forget about it! So we need a methodology. We almost always perform search using some type of search that we can describe as a tree search. Important concepts related to this approach: 1) state space - set of all possible configurations, including the initial state 2) node - a data structure for keeping track of how a state was reached during a search; typical contents of a node are: a description of state, a a description of or pointer to the node that preceded this one in the search; the action taken that led to the current state; g(n), the cost of getting to this state; the number of decisions made to get to this state from the initial state, also called the depth 3) search tree - a way to organize the sequence of actions taken during a search 4) search strategy - methodology for deciding what order potential actions will be explored: breadth first, uniform cost, depth first, depth limited, backtracking, iterative deepending Basic Search Algorithm 1) start in initial state; construct a search tree with one node corresponding to to the initial state 2) check to see if the current state is a goal state 3) use the Successor Function to generate a list of all the states that can be reached from the current state; add them to the search tree - the set of all unexplored nodes in the tree is called the Fringe 4) if fringe is empty, fail; else apply the chosen search strategy to choose a node from the fringe to become the active node and Go to Step 2 Apply Basic Search Algorithm to Sudoku --Talk through applying BFS and DFS to Sudoku -- Homework for discussion - which do you think will be faster? -1- C:\Userdata\www-home\csc371-671\ilp\L5_notes.txt Sunday, January 25, 2009 8:47 PM -2-

Find millions of documents here - Study Guides, Homework Solutions, Papers, Exam Answer Keys and more. Course Hero has millions of course related materials that will enable you to learn better, faster and get an A in all your courses.
Below is a small sample set of documents: