Analogy is a core cognitive capacity encompassing basic similarity (“this is like that”), relational similarity (proportional analogies of the form A:B::C:x), and complex system mappings, in which the elements of one situation are structurally aligned with the elements of another. The latter permits complex inferences from a known source situation to a less familiar target situation. Because of its centrality in human thinking, analogy has been the subject of numerous computational modeling efforts. Models of similarity come from multiple traditions in cognitive science, including associationist approaches (such as connectionist models), “traditional” symbolic approaches (such as graph matching and production systems), and hybrid symbolic/connectionist approaches. This chapter reviews and evaluates several models from these various approaches in terms of their ability to simulate basic similarity, relational similarity, and system mapping.

This chapter defines artificial intelligence and discusses its history and evolution, explains the differences between major types of AI (symbolic/classical and connectionist), and describes AI’s most recent advances, applications, and impact. It also weighs in on the question of whether AI can “think,” noting that the question is less relevant to regulatory efforts, which should focus on promoting behaviors that improve social outcomes.