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Symbolic AI

As explained in Inside AI

Symbolic AI is an artificial-intelligence approach that represents knowledge explicitly with symbols, rules, and logic.

Symbolic AI represents knowledge through explicit rules and logic. It addresses problems where pattern recognition from data is insufficient, especially when actions must satisfy constraints or follow deductive reasoning. section 7.0

Farmer, wolf, goat, and cabbage

The farmer must transport the wolf, goat, and cabbage across a river without allowing the wolf to eat the goat or the goat to eat the cabbage. Solving the puzzle requires determining a valid action sequence under predefined constraints. section 7.0

Data-trained systems can identify correlations yet lack inherent understanding of medical concepts, disease mechanisms, biology, pathology, and clinical complexity. Symbolic techniques therefore complement pattern-based AI where explicit knowledge matters. section 7.0

How Inside AI covers it

How deep it goes
Defines symbolic reasoning through a puzzle and contrasts it with statistical pattern recognition, without implementation details.
Examples
Uses the Farmer-Wolf-Goat-Cabbage puzzle and medical diagnosis to illustrate constraints and limits of data-trained systems.
What you can do after
Evaluate when explicit rules and logic are needed beyond statistical patterns and correlations.
How it connects
Connects symbolic AI with deductive reasoning, forward planning, pattern recognition, medical diagnosis, and smart agents.

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This explainer was generated by Manning's knowledge graph from the text of Inside AI. The section links point to the parts of the book it draws on.