Artificial Intelligence Programming Practice Exam 2025 - Free AI Programming Practice Questions and Study Guide

The term that describes approaches that do not effectively scale to larger or more complex problems is "Weak Methods." Weak methods refer to problem-solving strategies that are simplistic or lack in-depth knowledge about a problem domain. They often rely on general heuristics or rules of thumb, which can work well in smaller, more controlled environments or simpler problems but fail to generalize effectively to more complex situations.

In the context of artificial intelligence, weak methods may not leverage the substantial domain-specific information required to navigate complex problem spaces. As the scale or complexity of the problem increases, these methods often become inadequate, leading to inefficient problem-solving processes or entirely unsatisfactory results.

Genetic algorithms are typically adaptive and can be scaled to larger problems by evolving solutions across generations. Microworlds refer to simplified environments used for AI research, allowing testing and development within a limited scope, but they can be expanded. Physical symbol systems are more robust architectures that enable complex reasoning; thus, they are designed to scale effectively for a broader range of issues.