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Uncertainty in Artificial Intelligence

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  • Mon, 03/19/2018 - 3:00pm - 5:00pm

Uncertainty in Artificial Intelligence

PhD. Comprehensive Exam by:

Bryan St. Amour

Date: Monday, March 19th 2018
Time: 3:00 – 5:00 pm
Location:3105 Lambton Tower

Knowledge Representation and Reasoning is one of the oldest branches of Artificial Intelligence (AI). Early approaches to representing knowledge, for the sake of reasoning computationally, focused heavily on techniques rooted in and derived from mathematical logic. These early, symbolic approaches were found lacking when applied to solving real-world problems. Philosophical issues, such as the Symbol Grounding Problem, as well as practical issues, like the Frame Problem, motivated researchers to investigate alternative ways to capture the uncertainty and variability inherent in real world reasoning. In this comprehensive survey we provide a high level overview of uncertainty in AI, with focus given to numerical representations. Topics covered include: mathematical representations, computational uncertainty models, inference algorithms, and current research directions.

Thesis Committee:    
Internal Readers: Dr. Scott Goodwin and Dr. Mehdi Kargar          
External Reader: Dr. Abdulkadir Hussein
Advisors: Dr. Robert D Kent and Dr. Roman Maev

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