Answer Posted / Jaysingh Lalchand Prasad

Heuristic for rule learning refers to strategies or algorithms used to learn rules (decision trees, association rules) from a dataset. These heuristics are designed to optimize specific criteria such as information gain, entropy, or Gini index to produce rules that generalize well and minimize overfitting. On the other hand, Heuristics for decision making refer to strategies used by an agent to make decisions in situations where complete knowledge is unavailable. Examples include hill-climbing, beam search, and A* algorithm.

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