AI Methods

The main knowledge representation methods include: logical representation; semantic networks; frames representation; production rules; fuzzy logic.  Each of these branches is described in more detail in its separate entry

Probability and statistics are the fundamental mathematical tools that allow us to model, reason and proceed with inference in uncertain environments. Each probabilistic situation that we wish to analyse can be viewed in the context of any non-deterministic process  (experiment) that has a number of distinct possible outcomes. There are two possible approaches: Frequentist approach…

Machine learning represents one of the dominant areas of artificial intelligence. This is why the term is sometimes mistakenly taken to be a synonym for artificial intelligence. However, artificial intelligence is a broader term: it covers other areas apart from machine learning. Machine learning is an umbrella term for all methods and approaches that allow…

A class of methods often used to approach planning problems, constraint satisfaction problems, etc. by representing them as the problem of searching in some problem-specific space, where various alternatives can be tried until a solution is found.

Optimization is the process of finding parameters that minimize or maximize a certain criterion; the convention in optimization literature is to treat all problems as minimization problems (a maximization problem can easily be turned into a minimization problem by flipping the sign of the criterion). There are several families of methods that perform optimization. One…

Planning is one of the early application domains of artificial intelligence. Firstly, there has been a lot of work on how to represent planning problems in a standard form so that the same representation of a problem can be used with multiple planners. There is a number of planning languages – an early example is…