Analogical learning and automated rule constructions

This paper describes some experiments of analogical learning and automated rule construction. The present investigation focuses on knowledge acquisition, learning by analogy, and knowledge retention. The developed system initially learns from scratch, gradually acquires knowledge from its environment through trial-and-error interaction, incrementally augments its knowledge base, and analogically solves new tasks in a more efficient and direct manner.