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© 2000-2001 Franz Kurfess Project Topics 1 Topics for Master’s Projects and Theses -- Winter 2003 -- Franz J. Kurfess Computer Science Department Cal Poly Email: fkurfess@csc.calpoly.edufkurfess@csc.calpoly.edu
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© 2000-2001 Franz Kurfess Project Topics 2 Areas of Interest Artificial Intelligence knowledge management neural networks for structured knowledge Software Engineering component-based systems process-oriented design Educational Systems teaching support
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© 2000-2001 Franz Kurfess Project Topics 3 Specific Topics Knowlets Content-Based Spam Filtering Ontologies Neural Networks for Structured Knowledge How Computers Work AI Toolbox
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© 2000-2001 Franz Kurfess Project Topics 4 Knowlets component-based systems for knowledge management modular design of more complex systems from simpler components similar to components in Software Engineering, but the emphasis is on content, not function content-based organization of knowlet collections Background: AI, SE, possibly data bases Funding: possibly through CAD-RC other funding sources under investigation
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© 2000-2001 Franz Kurfess Project Topics 5 Content-Based Spam Filtering design and implementation of a system that categorizes email messages goal: identify as many unsolicited email messages (“spam”) as possible avoid false positives (valid messages mis-classified as spam) methods use content-based and possibly usage-based techniques rather than explicit rules to filter out spam Bayesian networks http://www.paulgraham.com/spam.html http://www.paulgraham.com/spam.html Collaborative filtering Background: AI, SE; possibly in combination with knowlets Funding: None
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© 2000-2001 Franz Kurfess Project Topics 6 Ontologies design and implementation of ontologies (semi-)automatic extension of ontologies user interfaces for ontologies various perspectives, presentation methods Background: AI, SE Funding: some through CAD-RC other funding sources under investigation
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© 2000-2001 Franz Kurfess Project Topics 7 Neural Networks for Structured Knowledge processing of complex structures representing knowledge with neural networks most NNs are based on vectors, and can’t represent knowledge easily recurrent NNs are more powerful, but also more difficult to handle experimentation with various types of NNs to evaluate their suitability bio-informatics (drug discovery, genome sequencing) knowledge management (ontologies, relationships between documents) Background: AI, specific domain Funding: None now
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© 2000-2001 Franz Kurfess Project Topics 8 How Computers Work demonstrations and animations of important concepts in computer science goal: visualize and animate abstract concepts and methods that may be difficult to understand from static text and diagrams hardware CPU, memory, hard disk, … OS algorithms CPU scheduling, disk scheduling, memory management, deadlock detection, … data structures and algorithms Background: Java Funding: Would be most welcome :-)
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© 2000-2001 Franz Kurfess Project Topics 9 AI Toolbox generic educational environment agents perform tasks in a simulated or real environment search for a goal, explore a room, perform a task, chase other agents, work in teams, … development of search algorithms, games, knowledge representation methods modular design playground, agents with various capabilities Background: AI, SE Funding: None
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