Artificial Intelligence Group

Welcome to the Artificial Intelligence Group

The Artificial Intelligence (AI) Group conducts research in many areas of artificial intelligence. The group has active interests in: models of intelligent interaction, multi-agent systems, natural language understanding, constraint programming, computational vision, robotics, machine learning, and reasoning under uncertainty.

  1. June 26, 2018Professor Robin Cohen receives Lifetime Achievement Award from Canadian Artificial Intelligence Association

    Professor Robin Cohen has received a Lifetime Achievement Award from the Canadian Artificial Intelligence Association. She is the first female recipient of the Association’s highest honour, an award that is conferred to individuals who have distinguished themselves through outstanding research excellence in artificial intelligence during the course of their academic career.

  2. June 14, 2018Perfectly sharp photo now a reality: Improved image selection for focus stacking in digital photography

    When you look at a scenic mountain photo typically everything in the distance is in sharp focus. But this scene might be even more captivating if something striking were in the foreground, perhaps a field of wild flowers in peak bloom. The problem is if the flowers are close to the lens relative to the mountains it’s impossible for all elements in the photo to be in perfect focus — if the flowers are sharp, the distant mountains will be blurry and vice versa.

  3. Apr. 24, 2018Michael Cormier wins the Murray Martin Prize for Best Research Paper by a Mathematics Graduate Student

    We live in a world increasingly dependent on the Internet for information retrieval, social interaction and general leisure. A growing number of Internet users with cognitive or visual impairments need assistive technology to make information accessible to them, but visually complex web pages can be difficult to navigate for assistive technology.

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  1. Sep. 4, 2018PhD Defence: On the Relationship Between Satisfiability and Partially Observable Markov Decision Processes

    Ricardo Salmon, PhD candidate
    David R. Cheriton School of Computer Science

    Stochastic satisfiability (SSAT), Quantified Boolean Satisfiability (QBF) and decision theoretic planning in infinite horizon partially observable Markov decision processes (POMDPs) are all PSPACE-Complete problems. Since they are all complete for the same complexity class, I show how to convert them into one another in polynomial time and space.

  2. Sep. 25, 2018PhD Defence: Learning Sparse Orthogonal Wavelet Filters

    Daniel Recoskie, PhD candidate
    David R. Cheriton School of Computer Science

    The wavelet transform is a well-studied and understood analysis technique used in signal processing. In wavelet analysis, signals are represented by a sum of self-similar wavelet and scaling functions. Typically, the wavelet transform makes use of a fixed set of wavelet functions that are analytically derived. We propose a method for learning wavelet functions directly from data. We impose an orthogonality constraint on the functions so that the learned wavelets can be used to perform both analysis and synthesis. We accomplish this by using gradient descent and leveraging existing automatic differentiation frameworks. Our learned wavelets are able to capture the structure of the data by exploiting sparsity. We show that the learned wavelets have similar structure to traditional wavelets.

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