Abstract
There is ample psychological evidence that analogy is ubiquitous in human learning, suggesting that computational models of analogy can play important roles in AI systems that learn in human-like ways. This talk will provide evidence for this, focusing mostly on recent advances in hierarchical analogical learning and working-memory analogical generalizations.
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