Research group’s website: http://ai.uwaterloo.ca
Group’s contact person: Gautam Kamath
- Shai Ben-David
- Yuri Boykov
- Dan Brown
- Wenhu Chen
- Robin Cohen
- Kimon Fountoulakis
- Ali Ghodsi (Department of Statistics and
- Maura R. Grossman
- Jesse Hoey
- Gautam Kamath
Artificial intelligence and machine learning are broad research areas within computer science that encompass a number of topics related to the design of computer systems that perform tasks conventionally associated with human intelligence. These areas overlap with several other research areas both within and beyond computer science, including algorithm design, information theory, statistics, optimization, scientific computation, human-computer interaction, and more. Some specific focuses of group members are listed below.
Machine learning is concerned with the analysis and development of methods to explore, discover, visualize, and model structure in data as well as to make predictions and decisions based on that structure. Data is often incomplete, noisy, non-homogeneous in structure and large in size (e.g., large number of observations or dimensions, or both). Special attention is paid to the development of computationally efficient (with respect to time and memory usage) algorithms. Research includes the mathematical and computational analysis of the statistical methodology, the development of new techniques, algorithms, and software, and the application of these to complex problems from other areas.
Intelligent user interfaces
Integrating natural language processing models and user models to produce more effective human-computer interaction. This includes designing interfaces that allow for mixed-initiative interaction. Applications include interface agents, electronic commerce and recommender systems.
Studying how computational limitations influence strategic behaviour in multi-agent systems, as well as developing approaches to overcome computational issues that arise in practical applications of mechanism design and game theory. Designing systems of collaborative problem-solving agents, with an emphasis on issues of communication and co-ordination for applications of multi-agent systems to the design of effective electronic marketplaces and adjustable autonomy systems. Modelling trust, reputation and incentives in multi-agent systems, including the use of social networks.
Natural language processing
The exploration of statistical and linguistic techniques to automate the analysis of natural text, the synthesis of clusters of documents, the retrieval of information from unstructured documents and the development of methods and software tools for computational rhetoric. Application domains include personalized mobile health and web analysis.
Investigating methodologies for solving difficult combinatorial problems by emphasizing modelling and the application of general purpose search algorithms that use constraint propagation. Current projects include instruction scheduling, constraint propagators for global constraints, and applying machine learning techniques to devise heuristics.
Developing computational theories of perception, based on Bayesian inference, preference rules, and qualitative probabilities, and applying such methods to problems in object recognition, motion estimation, and learning. Other work includes computational perception of scene dynamics, with applications in event recognition, human computer interaction, and robotics, the analysis and categorization of image motion, particularly in densely cluttered scenes, and the recognition of human behaviours in natural environments with application to assistive technology.
Decision-theoretic planning and learning
Design of algorithms to optimize a sequence of actions in an uncertain environment. The emphasis is on probabilistic and decision-theoretic techniques such as fully and partially observable Markov decision processes as well as reinforcement learning. Applications include assistive technology for persons with physical and cognitive disabilities and spoken-dialogue systems.
Studying how intelligent systems can be improved by reasoning about emotions. Investigating theories of culturally shared affective sentiments during human-machine interaction. Application areas include tutoring, sentiment analysis, assistive technologies and computational social science.
Human-in-the-loop intelligent systems
Across many AI-related research areas, various models combine human and machine intelligence to solve computational problems, including human computation (e.g., crowdsourcing), learning by demonstration, mixed initiative systems, active learning from human teachers, interactive machine learning, etc. In these systems, humans are a critical part of the computational process — they serve as teachers and collaborators to the AI system, providing feedback and corrections, or performing computational tasks that are difficult for existing algorithms. This area of research is at the intersection of AI, Human-Computer Interaction (HCI) and EconCS, involving the design of interfaces, algorithms and incentive mechanisms to harness human processing power to tackle challenging computational problems.