In the last post I talked about Basics of Artificial Intelligence https://panthimanshu17.wordpress.com/2013/07/25/artificial-intelligence-and-machine-learning-fundamentals-2/
In this post I will discuss about branches of Artificial Intelligence. As AI is continuously evolving it is very difficult to classify branches of Artificial Intelligence and the problem aggravates due to the fact that many branches of AI are overlapping in nature. Still I will try to classify them as neatly as possible.
Primarily there are three core branches Artificial Intelligence.
- Symbolic AI: the work in the field of symbolic AI is nearly abandoned, and reduced to mainly course books. In early stages of developments (1960s) symbolic AI tasted tremendous success with expert systems and game playing problems, but in 1980s the research nearly exhausted due to the lack of some implicit design issues in the formulation itself and it was assumed symbolic AI would never be able to achieve human cognition level. One major implicit design issue was “General Knowledge problem” or “common sense problem”. It means designers were able to mimic explicit human behavior into the machine but implicit common sense which we never say and take for granted which forms the base of explicit behavior was not conceptualized at the design stages, for example if I say that “she is my mother” it implicitly means that “I am her son” etc. CYC (CyCorp) project aims to beat this common sense problem.For more information on CYC project you can follow the link : http://www.cyc.com/time-digital.html
- Statistical AI: Statistical AI advocates deterministic approach in Artificial Intelligence taking inspiration from mathematics and operation research. critics argue that this approach looses capability of generalization and hence ultimate aim of Artificial Intelligence.
- 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 exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low cost solution with improved adaptability.Common branches of Computational Intelligence are but not limited to.
- Artificial Neural Networks
- Fuzzy Logic
- Heuristic Search
- Pattern Recognition
Well thats all for now. in coming posts I will be discussing more about Computational Intelligence and its branches.