Computational Intelligence, Soft Computing, Natural Computation are different names for more or less simular things, namely a couple of mathematical methods, which are geared to mechanisms and procedures of the nature. They are all characterized by an extremely high degree of functionality and robustness. Basically these methods comprise of
- Artificial Neural Networks,
- Evolutionary Strategies,
- Fuzzy Logic,
- Swarm Intelligence,
- Probabilistic Reasoning and Baysian Approaches,
- Chaos Theory.
Contrary to the classical, strictly formal branch of Artificial Intelligence these methods are more oriented on pragmatic, well functioning solutions and less on theoretical and formal clarity, which is anyway seldomly existend in "real-world applications". Thus most complicated tasks can be solved quickly and efficiently hereby. For a lot of complex problems these methods are often the only practically existing solution strategies.
- When does the application of Artificial Intelligence (AI) pay of?
- Artificial Neural Networks are Blackbox routines. Are you allowed to use them?
- What's so special about Artificial Neural Networks?
- How many data do you need for training an Artificial Neural Network?
- How to safeguard Artificial Neural Networks against unexpected extrapolation behaviour?
- Artificial Intelligence, Computational Intelligence, SoftComputing, Natural Computation - what's the difference?
- What's the difference between Machine Learning, Artificial Neural Networks, and Deep Learning?
- Why is adaptivity so important?
- What is an "intelligent system" and when is it allowed to talk about "Artificial Intelligence"?
- Optimization does not work, what can be done?
- Despite having excellent optimization results the real system fails totally – what went wrong?