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Research Artificial Intelligence COGNITIVE ARCHITECTURES & COMPUTATIONAL COGNITIVE SCIENCE CSE Faculty: John Laird, Satinder Singh Baveja Affiliated Faculty: David Kieras, Emily Mower Provost The Artificial Intelligence (AI) program at the University of Michigan comprises a multidisciplinary group of researchers conducting theoretical, experimental, and applied investigations of intelligent systems. Research in cognitive architecture studies the fixed structures underlying cognition. At Michigan, we are exploring architectural structures to support human-level AI systems, as well as computational models of human behavior and the structure of the human brain. All these efforts draw from AI, psychology, and neuroscience, so that our research is inherently interdisciplinary. Michigan is unique in the breadth of cognitive architecture research it supports, including active groups in Soar, EPIC, Act-R, Cognitive Constraint Modeling, and neutrally-inspired architectures. MULTIAGENT & ECONOMIC SYSTEMS Current projects include research in rational deci- CSE Faculty: Satinder Singh Baveja, Edmund Durfee, Michael Wellman sion making, distributed systems of multiple agents, Affiliated Faculty: Grant Schoenebeck machine learning, reinforcement learning, cognitive Environments with multiple autonomous agents present specialopportunities and pose distinct challenges for design and analysis of AI systems. An individual agent may coordinate with others to improve performance through intelligent selection of physical, communicative, and/or computational actions. The agent may also reason strategically, to predict what the other agents may do based on their presumed self-interests. A multiagent environment is effectively a social system, and thus analyzing multiagent behavior can often be informed by social science. Multiagent systems research at Michigan considers all perspectives, from individual agent to social designer. modeling, game theory, natural language processing, machine perception, healthcare computing, and robotics. Research in the Artificial Intelligence laboratory tends to be highly interdisciplinary, building on ideas from computer science, linguistics, psychology, economics, biology, controls, statistics, and philosophy. In pursuing this approach, laboratory faculty and students work closely with colleagues throughout the University. This collaborative environment, coupled with our diverse perspectives, leads to a valuable interchange of ideas within and across research groups. We design planning and learning algorithms suitable for multiagent contexts, and methods for analyzing networks of agents as organizations, economies, and societies. Our work also spans the range from theory to practice. We conduct fundamental research in distributed coordination, algorithmic game theory, and social computing, and apply our techniques to real-world problems in areas such as healthcare, electronic commerce, and finance. Bob and Betty Beyster Building 2260 Hayward Street, Ann Arbor, MI 48109-2121 cse.umich.edu Research Artificial Intelligence MACHINE LEARNING ROBOTICS Affiliated Faculty: Al Hero (ECE), Danai Koutra, Emily Mower Provost (CSE), Susan Murphy (Statistics), Ji Zhu (Statistics) Affiliated Faculty: Ella Atkins (Aero) CSE Faculty: Satinder Singh Baveja, Jia Deng, Danai Koutra, Benjamin Kuipers, John Laird, Honglak Lee, Jason Mars, Rada Mihalcea, Edwin Olson, Lingjia Tang, Jenna Wiens Research in machine learning at Michigan encompasses reinforcement, unsupervised, and supervised learning. In reinforcement learning, we focus on building autonomous agents that can learn to act in complex, sequential, and uncertain environments. In particular, a number of research projects derive from an interest in building long-lived and flexibly-competent agents rather than the more usual agents that perform one complex task repeatedly. In unsupervised learning, we focus on developing methods for automatically constructing deep and hierarchical feature representations of high-dimensional data with applications to computer vision and more generally to sensory information processing and perception. Other areas of interest include: 1) the integration of multiple learning methods into the cognitive architecture Soar; 2) developing specialized reinforcement learning methods for behavior-change and treatment-design in healthcare settings ; 3) developing specialized methods for learning in large-scale games and other multiagent problems; and 4) developing unsupervised, semi-supervised, and supervised learning algorithms for information retrieval and natural language processing as well as for computational biomarkers in medical domains. HEALTHCARE COMPUTING CSE Faculty: Satinder Singh Baveja, Honglak Lee, Jenna Wiens The shift towards data driven medicine will have a transformative impact on healthcare. Massive-scale analytical methods are needed for real-time disease tracking and for discovering new markers to risk stratify patients for adverse outcomes. The EECS Department and the Health System at Michigan provide a unique opportunity to carry out collaborative research at the cutting edge of machine learning, data mining, and medicine. CSE Faculty: Satinder Singh Baveja, Jason Corso, Edmund Durfee, Chad Jenkins, Benjamin Kuipers, John Laird, Edwin Olson We are investigating both theoretical and practical aspects of robots, including aerial, underwater, space, and terrestrial systems. Key areas include: 1) integration of strategic and tactical planning and optimization algorithms to enable robust robot control in the presence of system failures and environmental uncertainties; 2) simultaneous localization and mapping for mobile robots; 3) sensor processing algorithms, including feature matching, object detection, classification, and recognition; 4) AI methods from machine learning, cognitive architectures and multiagent systems to build autonomous robots. COMPUTER VISION CSE Faculty: Jia Deng, Chad Jenkins, Benjamin Kuipers, Honglak Lee, Edwin Olson We explore a number of critical problems in the area of computer vision. We focus on the analysis and modeling of visual scenes from static images as well as video sequences. Our research goals include: i) the semantic understanding of materials, objects, and actions within a scene; ii) modeling the spatial organization and layout of the scene and its behavior in time. The algorithms developed in this area of research enable the design of machines that can perform real-world visual tasks such as autonomous navigation, visual surveillance, or content-based image and video indexing. NATURAL LANGUAGE PROCESSING & INFORMATION RETRIEVAL CSE Faculty: Rada Mihalcea Affiliated Faculty: Steve Abney (Linguistics), Jason Mars (CSE), Qiaozhu Mei (SI), Lingjia Tang (CSE) We are interested in large-scale natural language processing, including information extraction, text summarization, question answering, and applications to other fields such as political science, bioinformatics, and social science. Our techniques range from semantic analysis and parsing to semi-supervised learning and graph-based methods. Some current projects include the automatic generation of surveys of scientific literature, protein network extraction from text, the extraction of attitude and sentiment in online social network discussions, the study of rumor propagation on Twitter, the dynamics of political speeches in US congress, and the creation of networks of genes, diseases, and vaccines. Bob and Betty Beyster Building 2260 Hayward Street, Ann Arbor, MI 48109-2121 cse.umich.edu Research Artificial Intelligence ASSISTIVE TECHNOLOGY CSE Faculty: Satinder Singh Baveja, Edmund Durfee, Walter Lasecki, Emily Mower Provost Artificial Intelligence techniques can significantly enhance technology that enables people with cognitive and/or physical impairments to live more autonomously. At Michigan, we employ automated planning, temporal reasoning, machine learning, probabilistic inference, and crowdsourcing techniques to create assistive technologies. Examples of target systems include ones that provide flexibly timed reminders of daily activities to people with memory impairment; that automatically adapt a computer interface to meet the needs of a person with visual or fine-motor impairment; that answer visual questions about the world for a blind person; or that monitor the behavior of a person at home, to identify emerging functional problems. CONSTRAINT-BASED REASONING CSE Faculty: Edmund Durfee, Michael Wellman Many interesting problems can be modeled as sets of constraints that must be satisfied and/or of preferences that should be optimized. Constraint-based reasoning studies both formalisms for representing constraints and preferences, and algorithms for efficiently finding solutions to problems encoded in these formalisms. Research on constraint-based reasoning at Michigan has focused on a variety of topics, including temporal reasoning, Boolean satisfiability, and Satisfaction Modulo Theory (SMT) solving, as well as on applications to varied domains such scheduling, interface design, and the verification of integrated circuits. HUMAN COMPUTATION & CROWDSOURCING CSE Faculty: Walter Lasecki Human computation incorporates human intelligence into algorithmic processes. It is, at its core, the study of structured work. While workflow, process management, and organizational theory has been developed in great depth previously, the introduction of computer science and computational theory has given us a new lens through which to view organized human efforts, and combine it with the efforts of automated systems. Crowdsourcing — the practice of making an open call to a group of workers to complete a task, often through social computing channels — has created the opportunity to create large-scale human computation systems that are far more capable than current automated systems, but are still available on-demand, at a moment’s notice. At Michigan, we are advancing the frontiers of what can be accomplished with human computation and crowdsourcing: exploring how to create systems that respond intelligently, in real-time, and with consistency over time. Bob and Betty Beyster Building 2260 Hayward Street, Ann Arbor, MI 48109-2121 cse.umich.edu Research Artificial Intelligence FACULTY IN ARTIFICIAL INTELLIGENCE Jacob Abernethy Assistant Professor jabernet 3765 Beyster Bldg Satinder Singh Baveja Professor baveja 3749 Beyster Bldg Jason Corso Associate Professor jjcorso 4227 EECS Jia Deng Assistant Professor jiadeng 3640 Beyster Bldg Edmund Durfee Professor durfee 3745 Beyster Bldg Chad Jenkins Associate Professor ocj 3644 Beyster Bldg Benjamin Kuipers Professor kuipers 3741 Beyster Bldg John Laird John L. Tishman Professor of Engineering laird 3753 Beyster Bldg Walter Lasecki Assistant Professor wlasecki 2636 Beyster Bldg Honglak Lee Associate Professor honglak 3773 Beyster Bldg Jason Mars Assistant Professor profmars 4705 Beyster Bldg Rada Mihalcea Professor mihalcea 3769 Beyster Bldg Emily Mower Provost Assistant Professor emilykmp 3620 Beyster Bldg Edwin Olson Associate Professor ebolson 3737 Beyster Bldg Lingjia Tang Assistant Professor lingjia 4609 Beyster Bldg Michael Wellman Professor wellman 3757 Beyster Bldg Jenna Wiens Assistant Professor wiensj 3640 Beyster Bldg Bob and Betty Beyster Building 2260 Hayward Street, Ann Arbor, MI 48109-2121 cse.umich.edu Research Artificial Intelligence AFFILIATED FACULTY Steven Abney - Linguistics Matthew Johnson-Roberson - Naval and Marine Engineering Ella Atkins - Aerospace Engineering Richard Lewis - Psychology and Linguistics Laura Balzano - Electrical and Computer Engineering Qiaozhu Mei - School of Information Dmitry Berenson – Electrical and Computer Engineering Kayvan Najarian - Computational Medicine and Bioinformatics Ceren Budak - School of Information Kevyn Collins-Thompson - School of Information Ryan Eustice - Naval and Marine Enigineering Alfred Hero - Electrical and Computer Engineering Danai Koutra - Computer Science and Engineering Mark Newman - School of Information Long Nguyen - Statistics Thad Polk - Psychology Ambuj Tewari - Statistics Bob and Betty Beyster Building 2260 Hayward Street, Ann Arbor, MI 48109-2121 cse.umich.edu