Abstract
Children behave dishonestly as a way of managing problems in daily life. Then our primary interest of this paper is how children learn dishonesty and how one could model human acquisition of dishonesty using machine learning techniques. We first observe the structural similarities between dishonest reasoning and induction, and then characterize mental processes of dishonest reasoning using logic programming. We argue how one develops behavioral rules for dishonest acts and refines them to more advanced rules.
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Sakama, C. (2013). Learning Dishonesty. In: Riguzzi, F., Železný, F. (eds) Inductive Logic Programming. ILP 2012. Lecture Notes in Computer Science(), vol 7842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38812-5_16
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DOI: https://doi.org/10.1007/978-3-642-38812-5_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38811-8
Online ISBN: 978-3-642-38812-5
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