What is Abductive Reasoning? Examples of Abduction and Induction

Abduction is the third type of logical reasoning we will discuss. After discussing the definition, I will give abductive reasoning examples and compare them to inductive and deductive reasoning. If you do not know inductive and deductive reasoning, you should consider reading the following articles first.


“Inference to the best explanation” is a compact and self-explanatory definition of abductive reasoning. Abductive reasoning aims to infer the explanations (e.g., causality). You can ask for a better definition… But if you, Oxford dictionary says: the action of forcibly taking someone away against their will. Okay, this is not what you asked. I could not find the definition in the Oxford dictionary. Let’s continue with the examples.

Examples – Abduction vs Induction vs Deduction

When I read about abduction, almost certainly, I encounter the diagnostic example. Doctors try to infer the disease from the symptoms of a patient. They try to abduct the most likely explanation (or diagnose the patient) based on the symptoms. You can better understand this example if you have watched House Tv series.

Statue of Sherlock Holmes, London

Another example is the reasoning of the detectives. Although Sir Arthur Conan Doyle emphasizes deductive reasoning, detective-type reasoning is one of the best examples of abductive reasoning. Just like doctors, Sherlock Holmes tries to come up with the best possible explanation. He says:

When you have eliminated the impossible, whatever remains, however improbable, must be the truth.

Sir Arthur Conan Doyle – Sherlock Holmes

A description of the abductive reasoning…

While induction aims to reach generalizations (theories) and deduction aims to reach a consequence of a known/assumed theory. So, abductive reasoning is a method to infer the relation between premises and conclusions. Understanding the difference between the three helps to understand abduction better.

I found the following example at Wikipedia:

PremiseAll the beans from this bag are pink.These beans are taken from this bag. (not all)All the beans from this bag are pink
PremiseThese beans are from this bag.These beans are pink.These beans are pink. (beans are not usually pink).
InferenceThese beans are pink. (True if premises are true)All the beans from this bag are pink. (May not be true).These beans are from this bag. (May not be true)
An example compares deduction, induction, and abduction.

In terms of the uncertainty of the inference, abduction and induction are similar, but they serve different purposes.

Artificial Intelligence and Abductive Reasoning

I call abductive “high-level logical reasoning.” The following analogy will help you better understand the difference between abduction, induction, and deduction.

We may think this in terms of the way computers work. The computer processors work with logic gates. You can see that how this relates to deductive reasoning. Deductive reasoning produces certain outcomes – no failure (Despite humans may fail at deductive reasoning). The processor is our computer working with the logic gates. By this means, processors are substitutes for human intelligence in certain ways. In fact, they are smarter in certain aspects. The clearest advantage is their computation capacity. However, it is not completely a substitute in this way.

It is still unclear how we can model high-level type of reasonings in AI studies

With the statistics models, however, we make computers smarter. Especially in recent years, with the increased computation capacity, we can train complex statistical models such as artificial neural networks (ANN). ANNs have interesting uses, making the “artificial intelligence” studies popular in the last decade. Image recognition, disease diagnostics, self-driving cars, and so on. These models are beyond what a human mind is capable of.

Abductive reasoning is one of the things very difficult for computers to do. A large computer with complex statistical models cannot achieve something we can. We do not know how to model computers so that they can infer causality and associate seemingly unrelated things. However, still, we all appreciate the sophistication of the human mind.

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