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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 with 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!

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Rule-based ML approaches include learning classifier systems, association rule learning, artificial immune systems, and any other method that relies on a set of rules, each covering contextual knowledge.

While the rule-based machine learning is a type of rule-based system in which it is often hand-crafted and other rule-based decision makers. This is because the rule-based applies some form of machine learning algorithm to automatically identify some useful rules, rather than a human being needing to apply prior domain knowledge to manually construct rules and curate a rule set.

Yes, rule-based learning 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 with well-established learning algorithms.

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