Is a rule-based system that learns considered reinforcement learning?

Question

I'm reading a book, "AI for Game Developers" by Glenn Seemann and David M Bourg, where they use video game AI as an example of a rule-based system which learns.

Essentially, the player has 3 possible moves, and hits in combos of three strikes. The AI is aiming to predict the player's third strikes. The rules of the system are all the possible 3-move combinations. Each rule has a "weight" associated to it. Every time the system guesses incorrectly, the weight of a rule is decreased. When the system has to pick a rule, it picks the rule with the highest weight.

How is this any different from a reinforcement-learning based system? Thanks!

Answer 1

Yes, this is reinforcement learning in the established use of the term. You may run into some opposition from those doing active research today, as the "hot" portions deal with deep learning applications.

Your application has a well-defined game tree to search; you can direct the reinforcements with a mathematical structure that corresponds directly to the game. This is a machine learning application, along well-established learning algorithms.

Current "hot" research is working with more complex game situations in which the correspondence between an action and its result is not well-defined. These video games use DL networks rather than game trees in an effort to eventually discover the action rules that will lead to higher success. They're solidly in the DL part of AI, which is why you see a partitioning in the things you read.