TY - GEN
T1 - Why learning can be hard
T2 - 2008 AAAI Fall Symposium
AU - Bonawitz, Elizabeth Baraff
AU - Schulz, Laura
PY - 2008
Y1 - 2008
N2 - Human intelligence has long inspired new benchmarks for research in artificial intelligence. However, recently, research in machine learning and AI has influenced research on children's learning. In particular, Bayesian frameworks capture hallmarks of children's causal reasoning: given causally ambiguous evidence, prior beliefs and data interact. However, we suggest that the rational frameworks that support rapid, accurate causal learning can actually lead children to generate and maintain incorrect beliefs. In this paper we present three studies demonstrating these surprising misunderstandings in children and show how these errors in fact reflect sophisticated inferences.
AB - Human intelligence has long inspired new benchmarks for research in artificial intelligence. However, recently, research in machine learning and AI has influenced research on children's learning. In particular, Bayesian frameworks capture hallmarks of children's causal reasoning: given causally ambiguous evidence, prior beliefs and data interact. However, we suggest that the rational frameworks that support rapid, accurate causal learning can actually lead children to generate and maintain incorrect beliefs. In this paper we present three studies demonstrating these surprising misunderstandings in children and show how these errors in fact reflect sophisticated inferences.
UR - http://www.scopus.com/inward/record.url?scp=77952150532&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77952150532&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:77952150532
SN - 9781577353980
T3 - AAAI Fall Symposium - Technical Report
SP - 27
EP - 34
BT - Naturally-Inspired Artificial Intelligence - Papers from the AAAI Fall Symposium, Technical Report
Y2 - 7 November 2008 through 9 November 2008
ER -