Exploring Symbolic AI: The Power of Programs That Think in Symbols

In the early days of AI, researchers proposed that intelligence could be captured through carefully designed symbol-processing programs, rather than by trying to replicate the human brain. This approach, known as Symbolic AI, relies on the idea that knowledge can be represented as symbols and rules that a program can manipulate to solve problems.

A classic example of Symbolic AI is the General Problem Solver (GPS), an early AI program created by cognitive scientists Herbert Simon and Allen Newell. GPS tackled logic puzzles—like the “Missionaries and Cannibals” problem—by using symbols and specific rules to represent and work through each step of the puzzle. In this setup, a human programmer defines symbols (e.g., missionaries, cannibals, riverbanks) and the rules governing these symbols (e.g., only two people can cross the river at once). The program then follows these rules step-by-step to reach the desired outcome.

What’s particularly intriguing is that these symbols hold no inherent meaning for the computer. For instance, if “MISSIONARIES” were replaced with a random code like “Z372B,” the program would still function identically. The “intelligence” of GPS is not in understanding but in processing symbols based on rules and logical steps.

Proponents of Symbolic AI argued that intelligence could be achieved through this abstract, symbolic system. By creating layers of symbols and rules, they believed a program could achieve general intelligence. This approach was influential in AI’s early decades, especially with the rise of “expert systems.” These specialised programs, used in fields like medicine or law, applied a set of human-devised rules to diagnose conditions or make legal decisions.

Today, symbolic methods still play a role, particularly in tasks requiring reasoning or clear rules. For example, they are used in medical diagnostic systems, legal analysis tools, and business process automation like fraud detection. In robotics, symbolic AI helps with planning tasks, and it supports technologies like the Semantic Web for organizing and retrieving information.

As AI has advanced, symbolic AI has been complemented—and sometimes challenged—by approaches like machine learning. However, its ability to provide transparency and logical reasoning keeps it relevant. Symbolic AI offers a fascinating perspective on how early AI aimed to build machines that could “think” by excelling at rule-following, even if not truly understanding.

Acknowledgements

The views expressed in this article are taken from the book Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell. All credit for this content goes to the author—I am simply sharing my summarized notes and key takeaways as I work through different sections of the book. Also, a big thanks to ChatGPT for turning my scribbled notes into something that actually makes sense! Since English isn’t my first language and I’m still learning how to write like a pro, ChatGPT is my trusty sidekick in making these insights clear and readable.