All you need to know about symbolic artificial intelligence

Today, artificial intelligence is mostly about artificial neural networks and deep learning. But this is not how it always was. In fact, for most of its six-decade history, the field was dominated by symbolic artificial intelligence, also known as “classical AI,” “rule-based AI,” and “good old-fashioned AI.”

Symbolic AI involves the explicit embedding of human knowledge and behavior rules into computer programs. The practice showed a lot of promise in the early decades of AI research. But in recent years, as neural networks, also known as connectionist AI, gained traction, symbolic AI has fallen by the wayside.

The role of symbols in artificial intelligence

Symbols are things we use to represent other things. Symbols play a vital role in the human thought and reasoning process. If I tell you that I saw a cat up in a tree, your mind will quickly conjure an image.

We use symbols all the time to define things (cat, car, airplane, etc.) and people (teacher, police, salesperson). Symbols can represent abstract concepts (bank transaction) or things that don’t physically exist (web page, blog post, etc.). They can also describe actions (running) or states (inactive). Symbols can be organized into hierarchies (a car is made of doors, windows, tires, seats, etc.). They can also be used to describe other symbols (a cat with fluffy ears, a red carpet, etc.).

Being able to communicate in symbols is one of the main things that make us intelligent. Therefore, symbols have also played a crucial role in the creation of artificial intelligence.

The early pioneers of AI believed that “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.” Therefore, symbolic AI took center stage and became the focus of research projects. Scientists developed tools to define and manipulate symbols.

Many of the concepts and tools you find in computer science are the results of these efforts. Symbolic AI programs are based on creating explicit structures and behavior rules.

An example of symbolic AI tools is object-oriented programming. OOP languages allow you to define classes, specify their properties, and organize them in hierarchies. You can create instances of these classes (called objects) and manipulate their properties. Class instances can also perform actions, also known as functions, methods, or procedures. Each method executes a series of rule-based instructions that might read and change the properties of the current and other objects.

Using OOP, you can create extensive and complex symbolic AI programs that perform various tasks.

The benefits and limits of symbolic AI

Symbolic artificial intelligence showed early progress at the dawn of AI and computing. You can easily visualize the logic of rule-based programs, communicate them, and troubleshoot them.