| BI-AMT 2009 Keynote Speakers |
 |
Using Neural Imaging to Inform the Instruction of Mathematics
Professor John Anderson
Department of Psychology, Carnegie Mellon University
http://act-r.psy.cmu.edu/people/ja/
Abstract:
I will describe research using fMRI to track the learning of
mathematics with a computer-based algebra tutor. I will describe the
methodological challenges in studying such a complex task and how we
use cognitive models in the ACT-R architecture to interpret imaging
data. I wll also describe how we can use the imaging data to identify
mental states as the student is engaged in algebraic problems
solving.
Profile:
John Anderson received his B.A. from the University of British
Columbia in 1968 and his Ph.D. from Stanford University 1972. He has
been at Carnegie Mellon University since 1978 where he is a professor
of psychology and computer science, and Richard King Mellon University
Professor of Psychology and Computer Science since 2001. He has been
a member of National Academy of Sciences of USA and a fellow of
American Academy of Arts and Sciences since 1999, and was Psychology
Section Chair of National Academy of Sciences (2001-2004), He was also
the president of Cognitive Science Society (1988-1989), and elected to
the American Philosophical Society in 2007. He received various
awards and prizes including American Psychological Association's
Distinguished Scientific Career Award (1994); the David E. Rumelhart
Prize for Contributions to the Formal Analysis of Human Cognition
(2004); Howard Crosby Warren Medal for outstanding achievement in
Experimental Psychology in the United States and Canada, Society of
Experimental Psychology (2005); and Dr. A.H. Heineken Prize for
Cognitive Science awarded by the Royal Netherlands Academy of Arts and
Sciences (2006). He has published a number of influential books
including Human Associative Memory (1973 with Gordon Bower), Language,
Memory, and Thought (1976), The Architecture of Cognition (1983), The
Adaptive Character of Thought (1990), Rules of the Mind (1993), The
Atomic Components of Thought (1998), and How Can the Human Mind Occur
in the Physical Universe? (2007). His current research is concerned
with developing the ACT-R theory of cognition and involves two related
enterprises. One effort is concerned with modeling the acquisition of
intellectual competences with major foci being the dynamic problem
solving skills such as in air traffic control and mathematical problem
solving skills. This research is also tied into efforts to develop
computer-based instructional systems. The second effort is concerned
with using fMRI brain imaging to track different components of the
cognitive architecture in the performance of complex tasks.
Distributed Human-Machine Systems: Progress and Prospects
Dr. Jeffrey M. Bradshaw
Florida Institute for Human and Machine Cognition (IHMC)
http://www.ihmc.us/users/jbradshaw
Abstract:
Advances in neurophysiological and cognitive science research have
fueled a surge of research aimed at more effectively combining human
and machine capabilities. In this talk we will give and overview of
progress and prospects for four current thrusts of technology
development resulting from this research: brain-machine interfaces,
robotic prostheses and orthotics, cognitive and sensory prostheses,
and software and robotic assistants. Following the overview, we will
highlight the unprecedented social ethics issues that arise in the
design and deployment of such technologies, and how they might be
responsibly considered and addressed.
Profile:
Dr. Jeffrey M. Bradshaw is a Senior Research Scientist at the Florida
Institute for Human and Machine Cognition (IHMC) where he leads the
research group developing the KAoS policy and domain services
framework. Formerly, he led research groups at The Boeing Company and
the Fred Hutchinson Cancer Research Center. He has been a Fulbright
Senior Scholar at the European Institute for Cognitive Sciences and
Engineering (EURISCO) in Toulouse, France; an Honorary Visiting
Researcher at the Center for Intelligent Systems and their
Applications and AIAI at the University of Edinburgh, Scotland; a
visiting professor at the Institut Cognitique at the University of
Bordeaux; is former chair of ACM SIGART; and former chair of the RIACS
Science Council for NASA Ames Research Center. He served as a member
of the National Research Council (NRC) Committee on Military and
Intelligence Methodology for Emergent Physiological and Cognitive/
Neural Science Research in the Next Two Decades and as a scientific
advisor to the Japanese NEC Technology Paradigm Shifts initiative. He
currently serves as an advisor to the HCI and Visualization program at
the German National AI Research Center (DFKI), and an external
advisory board member of the Cognitive Science and Technology Program
at Sandia National Laboratories. He is a member of the Technical
Committee for IEEE Systems, Man and Cybernetics, the IFIP Working
Group on HCI and Visualization, and for the Aerospace Human Factors
and Ergonomics of the IEA. Recently, he served as co-program chair for
Intelligent User Interfaces (IUI 2008) and as Program Vice Chair, 2008
IEEE International Conference on Distributed Human-Machine Systems
(DHMS 2008). He is co-chair for the 2009 Human-Agent-Robot Teamwork
Workshop, co-located with the International Conference on Human-
Robotic Interaction. Dr. Bradshaw serves on the Board of Directors of
the International Foundation for Autonomous Agents and Multiagent
Systems and is a member of the Parametric Human Consortium. He is on
the editorial board of the Journal of Autonomous Agents and Multi-
Agent Systems, the Web Semantics Journal, Schedae Informaticae, and
the Web Intelligence Journal, and was formerly on the board of the
Knowledge Acquisition Journal and the International Journal of Human-
Computer Studies. He led the DARPA and NASA funded ITAC study team
"Software Agents for the Warfighter" and has participated in NASA
Blue Sky Study Groups for the “Human-Centered Vision of Mars
Exploration” and for the “Small Pressurized Rover.” From 2002-2006,
KAoS was used as part of a NASA series of annual two-week field tests
of human-robot teams performing simulated planetary surface
exploration at the Mars Desert Research Station in the Utah
desert. Jeff was sponsored by DHS to undertake detailed simulation
studies of the use of human-robot teams to secure facilities at Port
Everglades. He has also led the ONR- sponsored NAIMT and Coordinated
Operations projects where a team of humans and heterogeneous robots
performed field exercises at the Naval Air Station in Pensacola, aimed
at port reconnaissance, and robot- assisted detection and apprehension
of intruders. Among hundreds of other publications, he edited the
books Knowledge Acquisition as a Modeling Activity (with Ken Ford,
Wiley, 1993), Software Agents (AAAI Press/The MIT Press, 1997).
Large Scale Reasoning on the Semantic Web:
what to do when success is becoming a problem
Professor Frank van Harmelen
AI Department, Vrije Universiteit Amsterdam
http://www.cs.vu.nl/~frankh
Abstract:
In recent years, the Semantic Web has seen rapid growth in size (many
billions of facts and rules are now available) and increasing adoption
in many sectors (government, publishing industry, media). This
success has brought with it a whole new set of problems: storage,
querying and reasoning with billions of facts and rules that are
distributed across different locations.
The Large Knowledge Collider (LarKC) is providing an infrastructure to
solve such problems. LarKC exploits parallelisation, distribution and
approximation to enable Semantic Web reasoning at arbitrary scale.
In this presentation we will describe the architecture and
implementation of the Large Knowledge Collider, we will give data on
its current performance, and we will describe a number of use-cases
that are deploying LarKC.
Profile:
Frank van Harmelen (1960) is a professor in Knowledge Representation &
Reasoning in the AI department (Faculty of Science) at the Vrije
Universiteit Amsterdam. After studying mathematics and computer
science in Amsterdam, he moved to the Department of AI in Edinburgh,
where he was awarded a PhD in 1989 for his research on meta-level
reasoning. While in Edinburgh, he co-developed a logic-based toolkit
for expert systems, and worked with Prof. Alan Bundy on proof planning
for inductive theorem proving. After his PhD research, he moved back
to Amsterdam where he worked from 1990 to 1995 in the SWI Department
under Prof. Wielinga, on the use of reflection in expert systems, on
the formal underpinnings of the CommonKADS methodology for
Knowledge-Based Systems. In 1995 he joined the AI research group at
the Vrije Universiteit Amsterdam, where he co-lead the On-To-Knowledge
project, on of the first Semantic Web projects. He was appointed full
professor in 2002, and is leading the Knowledge Representation and
Reasoning Group. He was one of the co-designers of the OWL Web
Ontology Language Language. He is currently scientific director the
LarKC project (http://www.larkc.eu), aiming to develop the Large
Knowledge Collider, a platform for very large scale semantic web
reasoning. His interests include approximate reasoning, Semantic Web,
medical protocols. He has published three books (on meta-level
inference, on knowledge-based systems, and on the Semantic Web) and
over 100 research papers, most of which can be found on-line.
How Midazolam Can Help Us Understand Human Memory:
3 Illustrations and a Proposal for a New Methodology
Professor Lynne Reder
Department of Psychology, Carnegie Mellon University
http://memory.psy.cmu.edu/
Abstract:
Midazolam is a benzodiazepine commonly used as an anxiolytic in
surgery. A useful attribute of this drug is that it creates
temporary, reversible, anterograde amnesia. Studies involving healthy
subjects given midazolam in one session and saline in another, in a
double-blind, cross-over design, provide insights into memory
function. Several experiments will be described to illustrate the
potential of studying subjects with transient anterograde
amnesia. This talk will also outline how this drug can be used in
combination with fMRI to provide more insights about brain functioning
than either method in isolation.
Profile:
Lynne Reder received her B.A. from Stanford University in 1972 and her
Ph.D. from the University of Michigan in 1976. She has been at
Carnegie Mellon University since 1978 where she is a professor of
psychology since 1992. She is a fellow of the American Psychological
Association, the American Association for the Advancement of Science,
and the Association for Psychological Science. The overarching theme
in her research is to further our understanding of human memory which
is at the heart of virtually all other psychological processes and
behaviors. She was a leader in demonstrating that people are adaptive
in selecting among different strategies to answer questions and solve
problems. A major focus in her lab concerns how information is
acquired and retrieved in different situations. She uses a variety of
methodologies including computational modeling, behavioral studies
that measure accuracy and latency, psychopharmacological interventions
(using midazolam that creates temporary anterograde amnesia),
functional magnetic resonance imaging (fMRI) and event related
potentials (ERP).
Research on Brain-like Computer
Professor Zhongzhi Shi
Key Laboratory of Intelligent Information Processing
Institute of Computing Technology, Chinese Academy of Sciences
http://www.intsci.ac.cn/en/shizz/
shizz@ics.ict.ac.cn
Abstract:
After more than 60 years of development, the operation speed of
computer is up to several hundred thousand billion times,
but its intelligence level is extremely low. Studying machine which
combines high performance and human high intelligence together
becomes the effective way with high capacity and efficiency of
exploring information processing. It will bring the important impetus
to economic and social sustainable development, promotion of the
information industry and so on to make breakthrough in the research of
brain-like computer.
Mind is all mankind's spiritual activities, including emotion, will,
perception, consciousness, representation, learning, memory, thinking,
intuition, etc. Mind model is for explaining what individuals operate
in the cognitive process for some thing in the real world. It is the
internal sign or representation for external realistic world. If the
neural network is a hardware of the brain system, then the mind model
is the software of the brain system. The key idea in cognitive
computing is to set up the mind model of the brain system, and then
building brain-like computer in engineering through structure,
dynamics, function and behavioral reverse engineering of the
brain. This talk will introduce the research progress of brain-like
computer, mainly containing intelligence science, mind model, neural
column, and architecture.
Profile:
Zhongzhi Shi is a professor at the Institute of Computing Technology,
the Chinese Academy of Sciences, leading the Research Group of
Intelligence Science. His research interests include intelligence
science, multiagent systems, semantic Web, machine learning and neural
computing. Professor Shi has published 13 monographs, 14 books and
more than 400 research papers in journals and conferences. He has won
a 2nd-Grade National Award at Science and Technology Progress of China
in 2002, two 2nd-Grade Awards at Science and Technology Progress of
the Chinese Academy of Sciences in 1998 and 2001, respectively. He is
a senior member of IEEE, member of AAAI and ACM, Chair for the WG 12.2
of IFIP. He serves as Vice President for Chinese Association of
Artificial Intelligence.
A Framework for Machine Learning with Ambiguous Objects
Professor Zhi-Hua Zhou
National Key Laboratory for Novel Software Technology
Nanjing University, China
http://cs.nju.edu.cn/zhouzh/
zhouzh@nju.edu.cn
Abstract:
Machine learning tries to improve the performance of the system
automatically by learning from experiences, e.g., objects or events
given to the system as training samples. Generally, each object is
represented by an instance (or feature vector) and is associated with
a class label indicating the semantic meaning of that object. For
ambiguous objects which have multiple semantic meanings, traditional
machine learning frameworks may be less powerful. This talk will
introduce a new framework for machine learning with ambiguous objects.
Profile:
Zhi-Hua Zhou is currently Cheung Kong Professor and Founding Director
of the LAMDA group affiliated with both the Department of Computer
Science & Technology and the National Key Laboratory for Novel
Software Technology at Nanjing University, China. He has wide research
interests, mainly including artificial intelligence, machine learning,
data mining, pattern recognition and information retrieval. In these
areas he has published over 70 papers in leading journals and
conferences. He has won various awards or honors. He is an associate
editor-in-chief of
, associate editor of
, and on the
editorial boards of (Elsevier),
(IOS), (Springer), , etc. He is the founder of
the ACML conference, Steering Committee member of PAKDD and PRICAI,
program committee chair/co-chair of PAKDD'07, PRICAI'08 and ACML'09,
vice chair or area chair of IEEE ICDM'06, IEEE ICDM'08, SIAM DM'09,
ACM CIKM'09, etc. He is the chair of the CAAI (Chinese Association of
Artificial Intelligence) Machine Learning Society, vice chair of the
CCF (China Computer Federation) Artificial Intelligence & Pattern
Recognition Society and chair of the IEEE Computer Society Nanjing
Chapter.
"Using Neural Imaging to Inform the Instruction of Mathematics"