Chapter 3 - Search

Abstract

This chapter provides an overview of the searching techniques used in artificial intelligence (AI). Search lies at the heart of AI, since almost all problems require a search to find a solution. Conventional mathematics can be used to find solutions to problems which have a mathematical representation, such as a formula or set of equations. Searching for solutions to the many other problems which do not have such a representation requires some understanding of the nature of the search space and how it is structured. This knowledge guides the selection of an appropriate search technique and becomes the basis of heuristics aimed at giving acceptable solutions most of the time within acceptable costs. This chapter elaborates the technical ideas underlying search in the context of finding solutions to problems and optimization. The chapter discusses about calculus-based search, the concept of hill climbing, and special forms of hill climbing such as gradient descent. Other methods that are discussed are relate to simulated annealing and genetic algorithms. The ideas of breadth-first search, depth-first search, and best-first search are discussed in detail. The chapter explains how heuristics can reduce the number of combinations, which have to be examined, and so make the search space smaller or easier to search.