Branches of Artificial Intelligence (AI)
The Search techniques are used extensively in AI programs. These programs examine a large number of possibilities and then pick the most optimal path. For example, this is used a lot in strategy games such as Chess, networking, resource allocation, scheduling, and so on. Discoveries are continually made about how to do this more efficiently in various domains.
2- Logical AI
Mathematical logic is used to execute computer programs in logic-based AI. A program written in logic-based AI is basically a set of statements in a logical form that express facts and rules about a particular problem domain. This is used extensively in pattern matching, language parsing, semantic analysis, and so on. The program decides what to do by inferring that certain actions are appropriate for achieving its goals.
A heuristic is a technique used to solve a given problem that’s practical and useful in solving the problem in the short term, but not guaranteed to be optimal. This is more like an educated guess on what approach we should take to solve a problem.
In Artificial Intelligence, we frequently encounter situations where we cannot check every single possibility to pick the best option. So we need to use heuristics to achieve the goal. They are used extensively in AI in fields such as robotics, search engines, and so on.
4- Pattern recognition
When a program makes observations of some kind, it is often programmed to compare what it sees with a pattern. For example, a vision program may try to match a pattern of eyes and a nose in a scene in order to find a face. More complex patterns. These more complex patterns require quite different methods than do the simple patterns that have been studied the most.
5- Statistical AI
Statistical AI advocates a deterministic approach in Artificial Intelligence taking inspiration from mathematics and operation research. critics argue that this approach loses the capability of generalization and hence the ultimate aim of Artificial Intelligence.
6- Knowledge representation
The facts about the world around us need to be represented in some way for a system to make sense of them. The languages of mathematical logic are frequently used here. If knowledge is represented efficiently, systems can be smarter and more intelligent.
7- Computational Intelligence
Computational Intelligence aims to solve real-world problems that are computationally expensive or not at all possible to solve by traditional means (mathematical models). The guiding principle of soft computing is to exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low-cost solution with improved adaptability.
This field deals with optimal planning that gives us maximum returns with minimal costs. These software programs start with facts about the particular situation and a statement of a goal. These programs are also aware of the facts of the world so that they know what the rules are. From this information, they generate the most optimal plan to achieve the goal.
9- Genetic programming
Genetic programming is a way to get programs to solve a task, by mating programs and selecting the fittest. The programs are encoded as a set of genes, using an algorithm to get a program that is able to perform the given task really well.