AI Explained

Chapter 2 Symbolic AI

In the aftermath of World War II, there was a significant shift in how societies perceived the role of experts and centralised planning. Emerging from the chaos of global war, many nations placed growing trust in expert-led systems built on logic, rationality, and formal rules.

Even in countries not rooted in socialist principles, there was a widespread belief that expert insights—grounded in structured, logical thinking—were key to solving the complex problems of rebuilding, stabilisation, and growth. Technocratic institutions run by specialists began to emerge in fields as varied as economics, infrastructure planning, defence strategy, and healthcare. This mindset, along with its broader themes and long-term effects, is often commonly referred to by contemporary historians as the post-war consensus.

The shift was mirrored in many areas and found expression within computer science through the emergence of various forms of symbolic AI—a traditional approach to AI based on symbols, rules, and logic. This early form of machine intelligence reflected a growing belief in the power of organisation, formal reasoning, and structured knowledge.

Overview

Before exploring the details of symbolic AI, we will begin with a concise overview of the key ideas, techniques, and motivations behind it. We start by introducing how symbolic approaches represent knowledge, apply rules, and support reasoning, giving you a clear sense of what symbolic AI is and why it matters, ...