Categorization reveals a lot about the field of study. However, Artificial Intelligence field has so diverse approaches to achieve its goals, presenting one nice and neat taxonomy of current existing diverse approaches is difficult. I will adapt classical classification of reasoning here like deduction, induction, and abduction. These names and taxonomy might not precise though, typical approach of these 3 reasoning covers a lot of AI algorithms, machine learning, and deep learning that is known as core algorithm of AlphaGo.
First of all, inductive reasoning is very typical approach with statistical machine learning such as KNN (K-nearest neighbor) or SVM (Support Vector Machine). He died and she died. Everyone died, so I will die is inductive reasoning. Likewise, these algorithms try to generate statistical functions and parameters, which can classify given data with training data (in other words, known answers).
Secondly, deductive reasoning requires many rules and facts. If all humans die and I am a human, then I will die. One of very similar machine learning algorithm to deductive reasoning is a decision tree. Decision tree algorithms try to generate rules based on known answers.
Third classification, abductive reasoning is similar to deep learning. He died and the cat died, so he is a cat is abductive reasoning. This logical output of this is incorrect reasoning example though, in real world, abductive reasoning shows great power since we cannot get whole information always. Deep learning use many example data to learn its pattern automatically at its memory network. (In other words, deep neural network)
We live with many kinds of artificial intelligence algorithms, and it includes deductive, inductive, and abductive reasoning. One type of reasoning cannot solve all problems. We have to choose algorithms selectively for the purpose of problems.
However, one thing very common to almost all algorithms is the needs of data. Extracting rules automatically, generating mathematical functions, and training deep neural networks needs data.
Fortunately, one human can produce valuable and meaningful data. (Yet!)
Now that you know 3 types of reasoning, find out about two conditions of AlphaGo’s learning curve here