Iterative Improvement Search Methods

Transcription

1 Iterative Improvement Search Methods Kris Beevers Intro to AI 9/18/03 Ch. 4.3 Overview Blind/heuristic search methods are designed to explore a search space systematically, and return a path to the goal as their solution In many problems, the path to the goal is not relevant: all we care about is arriving at the solution Iterative improvement algorithms: The solution is just a state in the search, not the path we took to get to it Start in a configuration in the state space, and try to improve it Often our goal is to maximize (or minimize) some objective (evaluation) function (i.e. optimization) Advantages of iterative improvement algorithms: Use very little memory (usually constant amount), because we only store the current state Often find reasonable solutions in large or infinite (continuous) state spaces for which systematic algorithms are unsuitable Objective Functions I.e. evaluation functions Returns a number given a state Generally not an analytic function; there are well-developed numerical optimization techniques for analytic functions, especially those with an analytic derivative State space landscape: draw and label a picture (p. 111) 1

2 Our state: a location on this landscape Our objective (depending on problem formulation) is usually to find either a global maximum or global minimum for this function Note that maximizing and minimizing are really equivalent: maximizing is the same as minimizing Usually any local maximum or minimum is a goal, but the optimal solution is global max or min Algorithm is complete if it always finds a goal if one exists (might/might not be more than one goal) Algorithm is optimal if it always finds a global minimum/maximum Hill-climbing Search More specifically, steepest-ascent hill climbing search Algorithm to find a local maximum: Given an initial problem state, create a search node for that state, call it current Repeat: Pick of with highest If, return (Show slide) Else, Does not maintain a search tree Stops when it reaches a peak where no neighbor has a higher value Note that if we are searching using some heuristic function that gives the cost from a state to the goal, we would try to minimize this function (gradient descent) Simple Variations Stochastic hill climbing: choose at random from among the uphill moves Vary probability of selection with steepness of uphill move Usually converges more slowly than steepest-ascent 2

3 For some landscapes, finds better solutions First-choice hill climbing: implements stochastic hill climbing by randomly generating successors until one is generated that is better than the current state Good strategy when a state has many successors (so we don t have to generate them all) Random-restart Hill-climbing: to find a global maximum, perform hill climbing from randomly selected initial states and take the best solution; often performs very well for somewhat simple landscapes Problems With Hill-climbing Searches Local maxima: i.e. peaks that are lower than the global minimum (slide) Ridges: sequence of local maxima that aren t connected to each other (slide) Plateus: might not be able to find our way off of the plateu Step sizes: Size of step may be dictated by the problem (e.g. 8-queens, 8-puzzle), or may be variable (continuous search spaces) Large step can converge more quickly, but small step can find maximum more accurately Direction of allowable steps affects efficiency and results (e.g. ridge example) Simulated Annealing A hill climbing algorithm that never makes a downhill move is guaranteed to be incomplete, because it can get stuck on a local maximum So, it might make sense to move downhill sometimes (Show slide with quote) Boltzmann probability distribution: Energy of a system in equilibrium at temperature is probabilistically distributed 3

4 Small chance of the system being in a high energy state even at low temperature Simulated annealing algorithm; at each step in an iterative improvement algorithm: Pick a random step (instead of the best step) If step improves (evaluation function), take it Otherwise, take the step anyway with probability that decreases exponentially with how much worse it is (and that is also affected by current temperature ) Cooling schedule idea: Temperature affects probability of taking a downward step every iterations) Use some cooling schedule to determine how the temperature decreases (e.g. decrease by Book has more formal implementation of algorithm (p. 116) Local Beam Search Rather than just keeping one node in memory at a time, keep track of states Algorithm: Begin with randomly generated states Repeat: Generate all successors of the current set of states If any one is a goal, return success Otherwise, select best successors from the complete list Not the same as running random restart searches in parallel! Here, useful information is passed among the parallel search threads E.g. if one state generates good successors and other states generate bad successors, we concentrate our resources on the good states Potential problem: lack of diversity of states; might quickly become concentrated in one small region of state space Stochastic beam search: choose successors at random, with probability proportional to how good they are 4

5 Genetic Algorithms Variant of stochastic beam search Generate successor states by combining two parent states (rather than by modifying a single state) Natural selection: Successors offspring States organisms Evaluation function value fitness Begin with a set of randomly generated states, the population Usually represent each state as a string over a finite alphabet (0s and 1s, or a set of predefined language elements, etc.) Each state is rated by the fitness function, which should return higher values for better states Usually select states for reproduction with probability proportional to their fitness Reproduction/mating: randomly choose crossover points in each parent; offspring gets part of one parent, part of the other When parents are quite different, offspring can be much different than either parent state; since often the population is diverse early in the process, crossover frequently takes large steps in the state space early in the search, small steps later on Each offspring is subject to mutation with small independent probability Problem: crossover can often destroy useful features Must engineer the representation of an organism carefully to minimize this type of problem 5