Learning path

Knowledge representation and reasoning (KRR)

Represent what a system knows in logic and networks, and derive new facts with entailment, chaining, resolution, unification, and probability.

About this subject

Knowledge representation and reasoning is about writing down what an agent knows in a form a program can use, then drawing conclusions from it. Different representations suit different needs: logic for precise rules, networks of concepts for categories and defaults, and probability for uncertain evidence.

This path covers propositional entailment by model checking, forward chaining on definite clauses, resolution refutation, semantic networks with default reasoning, first-order unification, and Bayes' rule with a small Bayesian network. Each lesson uses a small, complete program that runs on its own. Python has the full sequence. The other languages listed below add lessons that use that language's own idioms and standard library, such as Java threads, C++ smart pointers, C# LINQ, or Node.js streams, and appear only for the topics the language supports. The programs are simplified models chosen to make an idea visible, so they are a foundation for further study rather than a replacement for a full course or for production-grade software.

Before you start: Boolean logic, sets, and recursion.

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    Python

    Readable code for automation, data, and first programs.

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