Symbolic AI, also known as classical AI, is a type of artificial intelligence that uses symbols and rules for problem-solving and decision-making. It concentrates on replicating human intelligence by manually programming high-level knowledge into systems.


Imagine you’re playing a game where you have to pick up different objects and place them into the right boxes. You have a rulebook that tells you exactly where each object goes. Doesn’t matter what the new object is, you just follow the rulebook. That’s how Symbolic AI works, it uses definite rules to solve problems.

In-depth explanation

Symbolic AI, also known as Good Old-Fashioned Artificial Intelligence (GOFAI), leverages symbolic representations for cognitive tasks. Symbolic AI systems consist of a knowledge base that represents specific instances of facts. Knowledge in symbolic AI is explicit, with relationships defined by structures that symbolize reality and rules that guide the inference process.

Symbolic AI employs high-level, symbolic (human-readable) representations of problems. It operates using coded rules that guide its reasoning. For instance, a Symbolic AI system might be programmed with the rule “IF it is raining, THEN carry an umbrella”. It follows these predefined rules and decision trees in a top-down approach, hence earning its fame as a rule-based system.

These rules can be as simple as the aforementioned example, or incredibly complex, covering an enormous number of factors and potential scenarios. Symbolic AI systems are particularly efficient for problems that can be thoroughly defined according to a rule system, like certain games or logic problems.

However, despite their logical precision, symbolic AI systems are not flawless. They fail to model the ‘common sense reasoning’ exhibited by humans. Also, establishing a comprehensive rule system can be exhaustive and impractical in certain tasks like image recognition, where the complexity and variability of the problems significantly outweigh a rule-based approach.

Moreover, symbolic AI systems suffer from the ‘knowledge acquisition bottleneck’. They are heavily dependent on the completeness of their knowledge base and rule set. Any situation that falls outside of these predefined boundaries can be difficult for such a system to interpret or act upon.

Therefore, while symbolic AI systems are highly beneficial in structured, rule-governed environments, their efficiency dwindles in scenarios of uncertainty or tasks requiring a more intuitive approach.

Artificial Intelligence, Machine Learning (ML),, Rule-Based Systems, Expert Systems, Knowledge Base, Cognitive Architecture, Semantic Networks