Artificial Intelligence research at UR includes work on knowledge representation and reasoning, machine learning, dialog systems, statistical natural language processing, automated planning, AI-based assistive technology, and computer vision.  We collaborate with the departments of Linguistics and Brain and Cognitive Sciences, and participate in the Center for Language Sciences, the Center for Visual Science, and the Center for Computation and the Brain.

Faculty

  • James Allen: James' research interests span a range of issues covering natural language understanding, discourse, knowledge representation, common-sense reasoning, and planning. He has joint appointments in the Brain and Cognitive Sciences and Linguistics departments, holds the Dessauer Chair in Computer Science, and is a Fellow of the AAAI. He is the author of the definitive graduate-level textbook Natural Language Understanding, 2nd ed. (Benjamin Cummings, 1994).
  • Chris Brown: Chris's current activities center around building behaving, visually guided systems.  Recent projects include image inpainting, video segmentation, inferring depth from trajectories in multi-view imaging, and digital restoration of historic images.
  • George Ferguson: George is a Research Scientist in the department. His interests are in the development and application of AI, NL, and KR techniques in the construction of intelligent, collaborative computer systems. This includes applying or extending existing theories, such as for temporal reasoning or planning, to new problems, developing and implementing new theories where traditional ones break down, and creating frameworks in which such ``systems of systems'' can come together to support intelligent behaviour.
  • Daniel Gildea: Dan is interested in statistical approaches to natural language processing, in particular language understanding and machine translation. He has also worked on language and pronunciation modeling for speech recognition and computational approaches to phonology.
  • Henry Kautz: Much of Henry's research is concerned with fundamental questions of efficiency and scalability of knowledge representation and reasoning systems. He also heads the Laboratory for Assisted Cognition Environments, which aims to create computer systems to help people suffering from cognitive disabilities, such as the effects of Alzheimer's disease. This involves fundamental and applied research ranging over topics such probabilistic reasoning, plan recognition, ubiquitous computing, data fusion, user interfaces, and cognitive psychology.
  • Jiebo Luo: Jiebo's research spans image processing, computer vision, machine learning, data mining, medical imaging, and ubiquitous computing. He has been an advocate for contextual inference in semantic understanding of visual data, and continues to push the frontiers in this area by incorporating geo-location context and social context. A recent research thrust focuses on exploiting social media for machine learning, data mining, and human-computer interaction, for example, mining the wisdom of crowds for social, political, and economic prediction and forecasting. He has published extensively with over 180 papers and 60 US patents.
  • Randal Nelson: Randal's research interests involve machine vision and robotics with an emphasis on systems that engage in sensory-mediated interaction with the physical world. Current and recent projects include work on object recognition and learning representations at the interface between feature-based and appearance-based approaches, vision for manipulation and hand-eye coordination, motion recognition and analysis, and visual navigation.
  • Lenhart Schubert: Len's research interests center around language, knowledge representation, inference and planning. These interests are tied together by the general goal of developing agents with common sense and the ability to converse and acquire knowledge through language.

Recent Graduates of the Artificial Intellience Group

Project Pages

Project Name Brief Summary
Action Recognition from Unconstrained Amateur Videos

We developed a systematic framework for recognizing realistic actions from unconstrained amateur videos which have tremendous variations due to camera motion, background clutter, changes in object appearance and scale, and so on.

Boundary Extraction by Lineal Feature Growing

Computer vision method for extracting lineal features, both curved and straight, from an image using extended local information to provide robustness and sensitivity.

Digital Analysis and Restoration of Daguerreotypes

Cluster computing allows standard digital analysis and restoration techniques to be applied to high-resolution microscopic digitizations of Daguerreotypes from the collection of the George Eastman House in Rochester.  Knowing the image context of a feature (such as a small light spot) affects its probability of being noise (dust effect) or signal (foliage effect).  Machine learning can be used to automate some subtle decisions.

Interactive Co-segmentation of Topically Related Images with Intelligent Scribble Guidance

We developed a user-friendly system to facilitate a user to perform interactive segmentation of objects of interest from a group of related images by providing scribble guidance.

LACE: Laboratory for Assisted Cognition Environments

The goal of the Laboratory for Assisted Cognition Environments (LACE) is to create advanced computer systems that will enhance the quality of life of people suffering from cognitive disabilities. This interdisciplinary project combines computer science research in artificial intelligence and ubiquitous computing with clinical research on patient care.

Mining the Power of 'Like' in Social Media Networks

`Like' has now become a very popular social function on social media networks by allowing users to express their positive opinions of certain objects. It provides an accurate way of gauging user interests and an effective way of sharing or promoting information in social media. We developed a system called LikeMiner using a heterogeneous network model and related mining algorithms to estimate the representativeness and influence of objects.

Planning as Satisfiability

Solving combinatorially challenging planning problems by encoding as Boolean satisfiability and applying state of the art SAT solvers.

Automated Natural Language Translation

Methods for translating between natural languages (such as English and Chinese) by training statistical models on large collections of text.

Trains

The TRAINS project and its successors form one of the longest running research efforts on practical spoken dialogue: conversation undertaken with a specific task in mind.

Quagents: Intelligent Agents in a Quake World

Quagents are intelligent software agents that live in a simulated world provided by the Quake II game engine.