AI Bibliography
AI Bibliography |
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Crowder, J. A., Carbone, J., & Friess, S. (2020). Abductive artificial intelligence learning models. In Artificial Psychology (pp. 51–63). Springer. |
| Resource type: Book Article BibTeX citation key: Crowder2020 View all bibliographic details  |
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 |
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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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