Artificial Intelligence Lecture Md. Morshedul Islam Assistant Professor Department of Computer Science & Engineering Bangladesh University of Business and Technology (BUBT) April 15, 2017 Artificial Intelligence, Lecturer #13

Artificial Intelligence, Lecturer #13 Contents Informed Search Strategies Heuristic Information Hill Climbing Methods Best-First Search, etc. April 15, 2017 Artificial Intelligence, Lecturer #13

Informed Search Strategies Informed search algorithm have some idea of where to look for solutions. This uses problem specific knowledge and can find solutions more efficiently than uninformed search. These strategies often depend on the use of heuristic information (heuristic search function). Heuristic search function h(n), is estimated cost of the cheapest path from node n to goal node. April 15, 2017 Artificial Intelligence, Lecturer #13

Heuristic Information (1/2) Information about the problem: The nature of the states The cost of transforming from one state to another The promise of taking certain path The characteristics of the goals This information can often be expressed in the form of heuristic evaluation function f(n,g), a function of the node n and/or the goal g. April 15, 2017 Artificial Intelligence, Lecturer #13

Heuristic Information (2/2) Heuristics play an important role in search strategies because of the exponential nature of most problems. These help to reduce the number of alternatives from an exponential number to a polynomial number and thereby obtain a solution in a tolerable amount of time. April 15, 2017 Artificial Intelligence, Lecturer #13

Heuristic Search: Example Traveling salesman problem (Minimal distance tour): n-cities with path connecting the cities. A tour is any path which, begins with some starting city, visits each of the other cities exactly once, and returns to the starting city. A simple heuristic for choosing the next city at any point in a tour is one which picks the nearest unvisited neighbor. This policy gives no guarantee of an optimal solutions, but its solution is often good. Time required for this search is O(n2) [Normally exponential] April 15, 2017 Artificial Intelligence, Lecturer #13

Hill Climbing Methods (1/2) Hill climbing is like depth-first searching where the most promising child is selected for expansion. This method requires that some information be available with which to evaluate and order the most promising choices. At each point of the search path, a successor node that appears to lead most quickly to the top of the hill (goal) is selected for expansion. April 15, 2017 Artificial Intelligence, Lecturer #13

Hill Climbing Methods (2/2) When the children have been generated, alternative choices are evaluated using some type of heuristic function. The path that appears most promising is then chosen and no further reference to the parent or other children is retained. This process continues from node to node with previously expanded nodes being discarded. It terminates when it reaches a ‘peak’ where no neighbor has a higher value. April 15, 2017 Artificial Intelligence, Lecturer #13

Hill Climbing Methods: An Example 21 9 19 16 S 28 24 18 11 22 20 23 27 25 April 15, 2017 Artificial Intelligence, Lecturer #13

Hill Climbing Methods: Advantages & Disadvantages Save spaces: The algorithm does not maintain a search tree, so the current node data structure need only record the state and its objective function value. Disadvantages: Foothill trap: it occurs when local maxima or picks are found (Global maximum??). Ridge trap: It occurs when several adjoining nodes have higher values than surrounding nodes. Plateau trap: It occurs when all neighboring nodes have the same values. April 15, 2017 Artificial Intelligence, Lecturer #13

Artificial Intelligence, Lecturer #13 Best-First Search (1/2) Uses heuristic information to select most promising paths to goal node. Unlike hill climbing, this algorithm retains all estimates computed for previously generated nodes and makes its selection based on the best among them all. At any point in the search process, best-first moves forward from the most promising of all the nodes generated so far. April 15, 2017 Artificial Intelligence, Lecturer #13

Best-First Search (2/2) Algorithm Step-1: Place the starting node s on the queue Step-2: If the queue is empty, return failure and stop. Step-3: If the first element on the queue is a goal node g, return success and stop, Otherwise, Step-4: Remove first element from the queue, expand it and compute the estimated goal distances for each child. Place the children on the queue and arrange all queue elements in ascending order corresponding to goal distance from the front of the queue. Step-5: Return to step 2. April 15, 2017 Artificial Intelligence, Lecturer #13

Best-First Search: An Example L1: S20, S22, S28 18 16 15 S 12 28 22 20 10 8 2 25 5 L2: S35, S36, S38 L3: S40, S45 g April 15, 2017 Artificial Intelligence, Lecturer #13

Best-First Search: Advantages and Disadvantages If the evaluation function is accurate, then this will indeed be the best node. Its overcome the problem that occurs in hill climbing search Disadvantages: If the evaluation function will sometimes off , then this will lead the search astray. It is not optimal Note: Best-first search known as greedy best-first search April 15, 2017 Artificial Intelligence, Lecturer #13

Recommended Textbooks [Negnevitsky, 2001] M. Negnevitsky “ Artificial Intelligence: A guide to Intelligent Systems”, Pearson Education Limited, England, 2002. [Russel, 2003] S. Russell and P. Norvig Artificial Intelligence: A Modern Approach Prentice Hall, 2003, Second Edition [Patterson, 1990] D. W. Patterson, “Introduction to Artificial Intelligence and Expert Systems”, Prentice-Hall Inc., Englewood Cliffs, N.J, USA, 1990. [Minsky, 1974] M. Minsky “A Framework for Representing Knowledge”, MIT-AI Laboratory Memo 306, 1974. April 15, 2017 Artificial Intelligence, Lecturer #13

Questions or Suggestions? End of Presentation Questions or Suggestions? Thanks to all !!! April 15, 2017 Artificial Intelligence, Lecturer #13