Transcript Artificial Intelligence Group - Foundations of Programming

AI @ DePaul
Peter Wiemer-Hastings
[email protected]
312-362-5736
Faculty
•
•
•
•
•
•
Jacek Brzezinski
Robin Burke
Clark Elliott
Steven Lytinen
Craig Miller
Bamshad Mobasher
•
•
•
•
•
Ashley Morris
Joseph Phillips
Daniela Raicu
Noriko Tomuro
Peter WiemerHastings
Research Areas/Projects (1)
• Intelligent Information Retrieval / Filtering
– Web navigation (Miller)
– WebACE (Mobasher)
– ARCH (Mobasher, Lytinen, Miller)
– FAQFinder (Tomuro, Lytinen)
– Recommender systems (Burke)
• Intelligent Tutoring Systems, Cognitive
Modeling
– Miller
– Wiemer-Hastings
– Elliott
Research Areas/Projects (2)
• Natural Language Processing
– Unification grammar and parsing
(Tomuro, Lytinen)
– WordNet (Tomuro)
– Latent Semantic Analysis (WiemerHastings)
• Fuzzy GIS
– Morris
• AI in Games
– Brzezinski
Robin Burke
• Recommender systems
– Knowledge-based recommendation
– Hybrid recommender systems
– Interactive recommendation
• Applications in
– Electronic Commerce: Intelligent product
catalogs
– Digital Libraries: Intelligent multi-dimensional
browsing
Clark Elliott
• Emotion and Speech
– Natural Language Generation
– Natural Language Understanding
Steve Lytinen
• FAQFinder
– with Noriko Tomuro
– A natural language-based browser of
Frequently Asked Questions (FAQ) files
• A Unification-based Natural Language Parser
– with Noriko Tomuro
– Efficient parsing algorithms for a very
expressive grammar formalism called
Unification Grammar
• ARCH
– with Mobasher, Miller, Burke and Sieg
– Document retrieval using concept hierearchy
Craig Miller
• User modeling to evaluate interfaces
– in collaboration with NASA Ames
research labs
– Modeling of navigation patterns/behavior
of web users
– Evaluation of web site usability from a user's
perspective
• Cognitive models of human learning
– A rule-based category-learning system that produces
behavior consistent with human behavior
– Computational model of students interacting with an
educational program (electrostatic physics)
Bamshad Mobasher
• Research Interests
– Data mining and knowledge discovery
on the Web (Web Mining)
– Intelligent agents for information retrieval / filtering
– Agents for electronic commerce and automated
contracting
• Projects
– Automatic Web Personalization based on Web Usage
Mining
– MAGNET: Multi-agent distributed environment for
automated contracting and supply-chain management
– WebACE: a client-side Web agent for document
retrieval and categorization
Ashley Morris
• Using Fuzziness in Geographic
Information Systems (GIS)
– Able to better store and represent
spatial objects
• Fuzziness in data modeling
• Fuzzy learning systems
• http://morris2k.cti.depaul.edu/gis/FOOSBA
LL2.html
Joseph Phillips
•
Computational Scientific Discovery
– The field borrows from Philosophy of
Science, Machine Learning and
Knowledge Discovery in Databases (KDD).
–
–
–
–
–
Representing scientific knowledge
Automating scientific reasoning
Updating scientific models given data in databases
Visualizing models
Developing model building and preferencing criteria,
and defining heuristic functions over scientific
models.
Daniela Raicu
•
•
•
•
•
Content-based image retrieval
Computer vision
Data mining and knowledge discovery
Machine learning
Pattern recognition
Noriko Tomuro
• A Unification-based Natural
Language Parser
– with Steve Lytinen
– Efficient parsing algorithms for a very expressive
grammar formalism called Unification Grammar
• Computational Semantic Lexicon
– WordNet as the broad-coverage lexical resource
• FAQFinder
– with Steve Lytinen
– A natural language-based browser of Frequently
Asked Questions (FAQ) files
Peter Wiemer-Hastings
• Research Interests
– Natural Language Understanding
– Cognitive Modeling
– Artificial Intelligence in Education
• Projects (more info at
http://reed.cs.depaul.edu/peterwh)
– SLSA: Hybrid symbolic and vector-based
natural language understanding
– StoryStation: Helps children write better by
giving feedback from multiple agents
– RMT: Research Methods Tutor, currently
used by DePaul Psychology students
Classes (CSC)
• 3/457 (F) Expert Systems
– Learn how to make a rule-based system, and
some theory
• 3/458 (Sp) Symbolic Programming
– Learn Lisp and Prolog, basic AI langs
• 3/480 (W) Foundations of AI
– Search, logic, inference, agents
• 578 (F) Machine Learning
– ML and Neural Networks
• 587 (W) Cognitive Science
– Computer models of cognitive tasks
Other Classes
• DS/IS 575 (W) Intelligent Information
Retrieval
– How to pull important info out of the web or
some other large collection
• CSC 594 (Spr) Topics in AI
– This term: Topics in Knowledge Management
(Burke)
• ITS 427 (Spr) Information Processing
Models of Learning
– Learn about how people learn
• ITS 580 (?) Artificial Intelligence in
Learning Environments
– Intelligence in Education
Questions?