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Heuristic Search

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  1. Heuristic Search

  2. Introduction • George Polya defines heuristic as “the study of the methods and rules of discovery and invention” • In a state space search, heuristics are formalised as rules for choosing those branches in a state space that are most likely to lead to an acceptable problem solution.

  3. Introduction • AI problem solvers employ heuristics in two situations:- • A problem may not have an exact solution because of inherent ambiguities in the problem statement or available data. E.g. In medical diagnosis a given set of symptoms may have several possible causes; doctors use heuristics to choose the most likely diagnosis and formulate a plan for a treatment. • A problem may have an exact solution, but the coputational cost may be prohibitive.

  4. Heuristics • In many problems (such as chess), state space is combinatorial explosive, with number of possible states increasing exponentially with the depth of the search. Heuristics helps in this by guiding the search along the most “promising” path through the space.

  5. Heuristics • A Heuristic is a technique that improves the efficiency of search process. • Heuristics search techniques generally point in the interesting directions during the finding of solution to the problem or search process. • They normally provide the way to the good answer but not best solution to the problem.

  6. Heuristics • Heuristics sometimes might lead to miss point of interests the particular individuals. • Heuristics are improve the quality of the paths that are explored. • Using good heuristics, we can get good (though possibly nonoptimal) solutions to hard problems such as Tower of Hanoi or Cannibals and Missionaries.

  7. Heuristics • There are other two major ways in which you can incorporate domain specific heuristic knowledge and rule-based search • In the rules themselves. For example, the rules for a chess playing system might describe not simply the set of legal moves but rather a set of sensible moves as determined by the rule writer. • As a heuristic function that evaluates individual problem states and determines how desirable they are.

  8. Heuristic Function • A heuristic function is a function that that maps from problem state descriptions to measures of desirability, usually represented as numbers. • Which aspects of the problem state are considered, how they are evaluated, and the weights given to individual aspects are chosen in such a way that the value of the heuristic function at given node in the search process gives as good an estimate as possible of whether that node is on the desired path to a solution

  9. Heuristic Function • Well designed heuristic functions can play an important role in efficiently guiding the search process toward solution. • Some time a very simple heuristic functions can provide a failry good estimates of whether a path is of any good or not. • Figure below shows simple heuristic function for few problems.

  10. Heuristic Function

  11. Heuristic Function • The purpose of heuristic function is to guide the search process in the profitable direction by suggesting which path to follow first when more than one is available. • The more accurately heuristic function estimates the true merits of each node in the search tree (or graph), the more direct the solution process

  12. Heuristic Function • The solutions to AI problems were described as centering on search process. • However it can more precisely be described as a process of heuristic search.

  13. Problem Charasteristics • Heuristic search is a very general method applicable to a large class of problems. • It includes a variety of techniques, each of which is particularly effective for a small class of problems. • In order to choose the most appropriate method (or combination of methods) for a particular problem, it is necessary to analyse the problem along several key dimensions:

  14. Problem Charasteristics • Is the problem decomposable into a set of (nearly) independent smaller or easier or easier subproblems? • Can solution steps be ignored or at least undone if they prove unwise? • Is the problem’s universe predictable? • Is a good solution to the problem obvious without comparison to all other possible solutions?

  15. Problem Charasteristics • Is the desired solution a state of the world or a path to a state? • Is a large amount of knowledge absolutely required to solve the problems, or is knowledge important only to constrain the search? • Can a computer that is simply given the problem return the solution, or will the solution of the problem require interaction between the computer and a person?

  16. Is the problem decomposable? • Suppose we want to solve the problem of computing expression: ∫(x2 + 3x + sin2x + cos2x)dx • This problem can be solved by breaking it down into three smaller problems, each of which can be then solved by using small collection of specific rules. • The problem tree can be generated by the process of problem decomposition.

  17. Can solution steps be ignored or Undone? • Consider an attempt to solve the 8 – puzzle problem as shown below

  18. Can solution steps be ignored or Undone? • In attempting to solve the 8 – puzzle, we might make a stupid move. However we can backtrack and undo the first move.

  19. Is the Universe Predictable • In the 8 – puzzle problem when we make a move we know exactly what happen. i.e it is possible to plan an entire sequence of moves and be confident that we know what the resulting state will be. • We can use planning to avoid having to undo actual moves.

  20. Issue in the design of Search Process • Every search process can be viewed as a traversal of tree structure in which each node represents a problem state and each arc represents a relatinship between the states represented by nodes it connects. • The search process must find a path or paths through the tree that connect an initial state with one or more final states

  21. Issue in the design of Search Process • The tree that that must be searched could be constructed from the rules that define allowable moves in the problem space. • In practice most of tree is too large and most of it never be explored.

  22. Issue in the design of Search Process • Instead of first building the tree explicitly and then searching it, most search programs represent the tree implicitly in the rules and generate explicitly only those parts that they decide to explore.

  23. Important Issues in Search Techniques • Before understanding general-purpose search techniques the following are their important issues:- • The direction in which to conduct the search (forward versus backward reasoning). • We can search forward through the state space from the start sate to a goal state, or we can search backward from the goal.

  24. Important Issues in Search Techniques • How to select applicable rules (matching). Production systems typically spend most of their time looking for rules to apply so it is critical to have efficient procedures for matching rules against states. • How to represent each node of the search process (the knowledge representation problem and the frame problem)

  25. Summary • In general the first two steps that must be taken toward the design of a program to solve a particular problem: • Define the problem precisely. Specify the problem space, the operators for moving within the space, and the starting and goal state (s). • Analyse the problem to determine where it falls with respect to seven important issues

  26. Summary • The last two steps for developing program to solve that problem are: • Identify and represent the knowledge required by the task • Choose one or more techniques for problem solving, and apply those techniques to the problem.