Introduction to Abductive Inference in AI

Explore the fundamentals of abductive inference in this 45-minute lecture that introduces the concept from a computational perspective. Delve into the distinctions between deduction, induction, and abduction, and learn about various abduction problems, covering techniques, and the principle of parsimony. Discover different types of abduction, including logic-based, set covering, probabilistic, and consistency-based approaches. Examine specific applications such as geospatial abduction and probabilistic horn abduction, including its early use in perception in 1993. Gain insights into the challenges and future prospects of abductive inference in AI, concluding with closing remarks that synthesize key takeaways from this comprehensive introduction to abductive reasoning in artificial intelligence.

Introduction
Deduction vs. Induction vs. Abduction
Abduction problems, covering, and parsimony
Types of abduction logic-based, set covering, probabilistic, consistency-based
Geospatial abduction
Probabilistic horn abduction
Probabilistic horn abduction for perception in 1993!
Challenges and closing remarks