Having analyzed and reviewed a certain amount of articles and questions, apparently, the expression computational intelligence (CI) is not used consistently and it is still unclear the relationship between CI and artificial intelligence (AI).

According to IEEE computational intelligence society

The Field of Interest of the Computational Intelligence Society (CIS) shall be the theory, design, application, and development of biologically and linguistically motivated computational paradigms emphasizing neural networks, connectionist systems, genetic algorithms, evolutionary programming, fuzzy systems, and hybrid intelligent systems in which these paradigms are contained.

which suggests that CI could be a sub-field of AI or an umbrella term used to group certain AI sub-fields or topics, such as genetic algorithms or fuzzy systems.

What is the difference between artificial intelligence and computational intelligence? Is CI just a synonym for AI?


2 Answers 2


What is the difference between Artificial Intelligence and Computational Intelligence?

The short answer is that they are two parallel research efforts working on similar problems, but with different methodologies and histories. Essentially, they study similar things, but with different tools. In the modern context, computational intelligence tends to use bio-inspired computing, like evolutionary and genetic algorithms. AI tends to prefer techniques with stronger theoretical guarantees, and still has a significant community focused on purely deductive reasoning. The main area of overlap is in machine learning, especially neural networks.

The longer answer is that your source from 1948 says they are synonyms in part because it predates the split in the research community, which took place later.

The two communities have always some overlap in topics, but in my experience, mostly are skeptical of each other's methodologies, and mostly publish in separate journals. Some authors consider CI to be a subset of AI however, particularly those writing in the 1990s.

Example topics that are solidly in AI but definitely not in CI are logical and expert systems, and statistical approaches to machine learning like regression.

Example topics that are solidly in CI but perhaps not in AI (depending on whether one views CI as a subset of AI or not) are genetic programming, fuzzy logic, and ant colony optimization.

As a rule, AI-rooted techniques have better theoretical guarantees, and better developed theory in general (there are exceptions though). For example, Fuzzy Logic has been strongly criticized for the lack of a solid theoretical foundation (good modern summary here), as have genetic and evolutionary approaches (most famously, both lack a proof of convergence within finite time to a global optimum on a smooth surface, even though they do quite well in practice).

CI-rooted techniques nonetheless often see major performance advantages in specific problems (see, for instance, deep learning results), and tend to have a strong experimental and engineering tradition. The No Free Lunch theorems are often used to justify their use when theoretical certainty is missing. Basically, the theorems say that, in learning and optimization problems, a technique can only perform well on a problem by performing poorly on some other problem. CI authors argue that there are some problem domains in which their techniques work well (which must be true, because simpler algorithms like hill-climbing outperform them on simple problems).

Check out this paper for lots more references on CI, or this book for a list of core topics in the field.

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    – nbro
    Mar 7, 2020 at 4:13

The book Computational Intelligence: An Introduction (2nd edition, 2007) by Andries P. Engelbrecht, which has been cited more than 3000 times, defines artificial intelligence as follows

These intelligent algorithms include artificial neural networks, evolutionary computation, swarm intelligence, artificial immune systems, and fuzzy systems. Together with logic, deductive reasoning, expert systems, case-based reasoning and symbolic machine learning systems, these intelligent algorithms form part of the field of Artificial Intelligence (AI). Just looking at this wide variety of AI techniques, AI can be seen as a combination of several research disciplines, for example, computer science, physiology, philosophy, sociology and biology.

and computational intelligence as follows

This book concentrates on a sub-branch of AI, namely Computational Intelligence (CI) – the study of adaptive mechanisms to enable or facilitate intelligent behavior in complex and changing environments. These mechanisms include those AI paradigms that exhibit an ability to learn or adapt to new situations, to generalize, abstract, discover and associate. The following CI paradigms are covered: artificial neural networks, evolutionary computation, swarm intelligence, artificial immune systems, and fuzzy systems.

He then notes

At this point it is necessary to state that there are different definitions of what constitutes CI. This book reflects the opinion of the author, and may well cause some debate. For example, swarm intelligence (SI) and artificial immune systems (AIS) are classified as CI paradigms, while many researchers consider these paradigms to belong only under Artificial Life. However, both particle swarm optimization (PSO) and ant colony optimization (ACO), as treated under SI, satisfy the definition of CI given above, and are therefore included in this book as being CI techniques. The same applies to AISs.

So, there may be different definitions of CI (given by different people), but, given that this book has been cited so many times, I would just stick to these definitions and use this book as a reference (I have actually consulted it a few times in the past). My university library even contains a copy of it.

To summarise, CI is a sub-field of AI, which studies (or is associated with) the following topics

  • artificial neural networks (NN),
  • evolutionary computation (EC),
  • swarm intelligence (SI),
  • artificial immune systems (AIS), and
  • fuzzy systems (FS).

which are also part of AI, which additionally studies

  • logic,
  • deductive reasoning,
  • expert systems,
  • case-based reasoning, and
  • symbolic machine learning systems.

Just to give further credibility to these definitions, Andries P. Engelbrecht has an h-index of 59, has been cited 22557 times, and is an IEEE Senior Member. You can find more info about him here. Note that I have no affiliation with him. I am just providing this information so that people start to follow these definitions (rather than just looking at definitions given by people who have not extensively studied the field). Moreover, note that the definition of CI given by Engelbrecht is consistent with the definition given by IEEE that you are quoting.


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