Search and optimization Artificial intelligence

many problems in ai can solved in theory intelligently searching through many possible solutions: reasoning can reduced performing search. example, logical proof can viewed searching path leads premises conclusions, each step application of inference rule. planning algorithms search through trees of goals , subgoals, attempting find path target goal, process called means-ends analysis. robotics algorithms moving limbs , grasping objects use local searches in configuration space. many learning algorithms use search algorithms based on optimization.

simple exhaustive searches sufficient real world problems: search space (the number of places search) grows astronomical numbers. result search slow or never completes. solution, many problems, use heuristics or rules of thumb eliminate choices unlikely lead goal (called pruning search tree ). heuristics supply program best guess path on solution lies. heuristics limit search solutions smaller sample size.

a different kind of search came prominence in 1990s, based on mathematical theory of optimization. many problems, possible begin search form of guess , refine guess incrementally until no more refinements can made. these algorithms can visualized blind hill climbing: begin search @ random point on landscape, , then, jumps or steps, keep moving our guess uphill, until reach top. other optimization algorithms simulated annealing, beam search , random optimization.

evolutionary computation uses form of optimization search. example, may begin population of organisms (the guesses) , allow them mutate , recombine, selecting fittest survive each generation (refining guesses). forms of evolutionary computation include swarm intelligence algorithms (such ant colony or particle swarm optimization) , evolutionary algorithms (such genetic algorithms, gene expression programming, , genetic programming).