Python Implementation of Algorithms from Artificial Intelligence: A Modern Approach: A Practical Guide
Since its first publication, Stuart Russell and Peter Norvig’s Artificial Intelligence: A Modern Approach (AIMA) has served as the definitive “map” for the field. However, for a programmer in 2026, the real challenge isn’t just understanding the pseudocode on the page—it is translating those abstract concepts into efficient, readable, and “Pythonic” code.
While the textbook provides the logic, the aima-python repository (the official companion project) provides the architecture. This article explores how to implement these algorithms using a modern approach that emphasizes modularity, immutable states, and the power of the Python ecosystem.
The Infrastructure of an AI Problem
In the AIMA framework, an agent doesn’t just “run code”; it solves a Problem. To implement any search-based algorithm, we must first build a formal bridge between the textbook’s definitions and Python’s class structures.
The core of any search implementation relies on two primary classes: Problem and Node.
- The Problem Class: