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Rete Algorithm

note

Generated by ChatGPT, leaving it here for my own reference

Example Scenario​

  • Imagine you have a rule engine for a pet store, and you're applying the following rules to determine discounts:

    • Rule 1: If a customer buys more than 5 items, they get a 10% discount.
    • Rule 2: If a customer buys more than 10 items, they get a 15% discount.

Facts​

  • Customer A buys 4 items.
  • Customer B buys 6 items.
  • Customer C buys 12 items.

How Rete Algorithm Works​

Building the Rete Network​

  • When the rules are added to the system, the Rete algorithm constructs a network of nodes.
  • These nodes represent the conditions in the rules.
  • For our example, there might be nodes that represent the "number of items purchased" condition.

Adding Facts​

  • When a new fact (like a customer purchase) is introduced, it's passed through this network.

Nodes Evaluate Facts​

  • Each node in the network evaluates the fact against its condition. For instance, a node might check, "Is the number of items greater than 5?"

Propagation Through the Network​

  • As the fact moves through the network, only paths where conditions are met are followed.
    • Customer A (4 items), neither rule is applicable, and the fact doesn't propagate to the action nodes.
    • Customer B (6 items), it meets the condition of Rule 1 but not Rule 2, so it propagates only to the action node of Rule 1.
    • Customer C (12 items), it meets both conditions and propagates to both action nodes.

Action Nodes​

  • At the end of the network are action nodes.
  • If a fact reaches an action node, the corresponding action (like applying a discount) is executed.

Efficiency​

The Rete algorithm's strength lies in its ability to remember the outcomes of these condition checks. When a new fact is introduced, or an existing fact changes, not all conditions need to be re-evaluated. This makes it extremely efficient for scenarios where many facts are evaluated against a stable set of rules.