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Crowder, J. A., Carbone, J., & Friess, S. (2020). Abductive artificial intelligence learning models. In Artificial Psychology (pp. 51–63).Springer. 
Added by: SijanLibrarian (2021-08-30 09:23:51)   Last edited by: SijanLibrarian (2021-08-30 09:25:52)
Resource type: Book Article
BibTeX citation key: Crowder2020
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Categories: Artificial Intelligence, Cognitive Science, Complexity Science, Computer Science, Data Sciences, Decision Theory, General
Subcategories: Autonomous systems, Decision making, Deep learning, Edge AI, Machine intelligence, Machine learning, Neurosymbolic, Q-learning
Creators: Carbone, Crowder, Friess
Publisher: Springer
Collection: Artificial Psychology
Views: 45/51
Views index: 22%
Popularity index: 5.5%
Abstract
There has been much research in recent years in the applicability of abductive reasoning to artificial intelligence and machine learning. Abductive learning involves finding the best explanation for a set of observations, based on creating a set of possible explanatory hypotheses. Formal models have been created (Abe, Proceedings of the IJCAI97 Workshop on Induction, 1997), which are utilized to analyze the properties and computational efficiencies of abductive reasoning to various artificial intelligence applications. Here we discuss machine learning models based on abductive learning techniques and their implications to artificial reasoning.
  
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