Computer Sciences Lab

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  • For your info, the picture is one of SyDRe, a supply restoration system which locates faults in power distribution systems and reconfigure them appropriately. An example of this is show in the little "Restore" window, where SyDRe says there is a fault on line l6 and suggest a reconfiguration plan opening and closing some switches.
  • Computer Sciences Lab

    1. 1. Computer Sciences Lab & NICTA Opportunities for Honours projects 2009 Marcus Hutter (adapted from and thanks to Sylvie Thiebaux)
    2. 2. College of Engineering & Computer Science (& Friends) DCS DEng CSL InfoEng NICTA
    3. 3. Research Groups <ul><li>Research focuses on: </li></ul><ul><ul><li>Artificial Intelligence </li></ul></ul><ul><ul><li>Machine Learning </li></ul></ul><ul><ul><li>Logic & Automated Reasoning </li></ul></ul><ul><ul><li>Computer Vision </li></ul></ul><ul><li>40 researchers in 4 groups: </li></ul><ul><ul><li>Diagnosis, Planning, & Optimisation (DPO, aka KRR) </li></ul></ul><ul><ul><li>Statistical Machine Learning (SML) </li></ul></ul><ul><ul><li>Logic & Computation (LC) </li></ul></ul><ul><ul><li>Vision Science, Technology & Applications (VISTA) </li></ul></ul>
    4. 4. Diagnosis, Planning & Optimisation Group <ul><li>Diagnosis </li></ul><ul><ul><li>Explain abnormal situations from observations </li></ul></ul><ul><ul><li>Circuits, power networks, web services, humans </li></ul></ul><ul><li>Planning </li></ul><ul><ul><li>Decide & schedule the tasks to be undertaken to meet given objectives </li></ul></ul><ul><ul><li>Project planning, military operations planning, robot control, solving puzzles & games </li></ul></ul><ul><li>Optimisation </li></ul><ul><ul><li>Find the best possible solution to a problem </li></ul></ul><ul><ul><li>How can we predict the hardness of optimisation problems? </li></ul></ul>Contact: Jussi.Rintanen@nicta.com.au
    5. 5. Model-Based Supervision of Composite Systems Composite systems : feature simple components organised into a highly reconfigurable architecture Examples : web & grid services, power and water systems telecom networks, traffic control systems <ul><li>Supervision tools : confer the ability to </li></ul><ul><ul><li>self-diagnose to detect faults in the system and explain their causes </li></ul></ul><ul><ul><li>self-reconfigure to restore or improve service </li></ul></ul>Project goals : develop theories, algorithms & tools for the supervision of composite systems Approach : draws on artificial intelligence (model-based diagnosis, planning), discrete- event systems, and model-checking Contact: Jussi.Rintanen@nicta.com.au
    6. 6. Statistical Machine Learning Group <ul><li>Machine Learning automates the input-output mapping. </li></ul><ul><li>Lots of fun projects for analysing data. Let us do both theory and application </li></ul>input(data) Documents Video Molecules Microarrays Sensor Networks Mission Plans output(analysis) Authors, script People, scenes Biological function Cancer diagnosis Novelty, alarm Optimal strategy Magic happens … Contact: Wray.Buntine@nicta.com.au
    7. 7. Document Analysis <ul><li>. </li></ul><ul><li>Build document similarity measure </li></ul><ul><li>Build fast discriminative optimiser (SVM style) </li></ul><ul><li>Integrate into mail filtering system (e.g. DSPAM) </li></ul>ab c$ b c$ c$ abc$ abc$ + = Spam filter Suffix tree Contact: Wray.Buntine@nicta.com.au
    8. 8. Logic and Computation Group <ul><li>Logical analysis of systems </li></ul><ul><ul><li>Assure correctness, safety, robustness </li></ul></ul><ul><ul><li>Software systems (are votes counted okay?) </li></ul></ul><ul><ul><li>Physical systems (will the robot arm break?) </li></ul></ul><ul><ul><li>Trust and Security (can I trust this eBay seller?) </li></ul></ul><ul><li>Tools for reasoning by computers </li></ul><ul><ul><li>Logical deduction: “Does it follow?” </li></ul></ul><ul><ul><li>Constraint satisfaction: “How might it be?” </li></ul></ul><ul><li>Theory behind all this </li></ul><ul><ul><li>New kinds of logic for new tasks </li></ul></ul>Contact: Rajeev.Gore@rsise.anu.edu.au
    9. 9. Constraint Satisfaction Platform (G12) <ul><li>Constraint Satisfaction Problem </li></ul><ul><ul><li>“Hard” constraints - e.g. every team plays every other at home and away </li></ul></ul><ul><ul><li>“Soft” constraints - e.g. fairness conditions (may be complex) </li></ul></ul><ul><ul><li>Additional requirements from TV stations, etc. complicate further </li></ul></ul><ul><li>Difficult computational problem </li></ul>Contact: John.Slaney@anu.edu.au
    10. 10. L4 Verified <ul><li>L4 Micro-kernel </li></ul><ul><ul><li>L4 operating system used in embedded systems </li></ul></ul><ul><ul><ul><li>e.g. sensor networks, mobile phones </li></ul></ul></ul><ul><ul><li>“ Small” trusted kernel (guarantees separation properties) </li></ul></ul><ul><li>NICTA project: formally verify the kernel </li></ul><ul><ul><li>Project runs until 2008 </li></ul></ul><ul><ul><li>One of the most ambitious formal verification projects ever undertaken anywhere </li></ul></ul><ul><ul><li>Commercial potential if successful </li></ul></ul>Contact: Michael.Norrish@nicta.com.au
    11. 11. Vision Science, Tech. & Applications Group <ul><li>Major projects: </li></ul><ul><ul><li>Spectral imaging </li></ul></ul><ul><ul><li>Smart cars </li></ul></ul><ul><ul><li>Medical image analysis </li></ul></ul><ul><ul><li>Surveillance </li></ul></ul>Contact: Nick.Barnes@nicta.com.au
    12. 12. Smart Cars Pedestrian detection & tracking Speed sign detection & recognition Car detection & tracking A complete driver assistance system, focusing on driver safety Contact: Lars.Petersson@anu.edu.au
    13. 13. Automatic Anatomical Structure Extraction Topology repair Parametrisation <ul><li>Detection of Alzheimer’s disease </li></ul><ul><ul><li>changes to hyppocampus implicated - doctors hand-trace each scan slice - obtain a math. representation for analysis - need to repair and parametrise the 3D data </li></ul></ul>Contact: Paulette.Lieby@nicta.com.au
    14. 14. Artificial Intelligence <ul><li>Universal Artificial Intelligence </li></ul><ul><li>= = </li></ul><ul><li>Decision Theory = Probability + Utility Theory </li></ul><ul><li>+ + </li></ul><ul><li>Universal Induction = Ockham + Bayes + Turing </li></ul><ul><li>Information-theoretic, </li></ul><ul><li>Statistical, and </li></ul><ul><li>Philosophical, </li></ul><ul><li>Foundations of </li></ul><ul><li>Artificial Intelligence </li></ul>Contact: Marcus.Hutter@anu.edu.au
    15. 15. Finally … <ul><li>These slides are at: http://www.hutter1.net/rsise/honours.ppt </li></ul><ul><li>Many other projects, for exmple in: </li></ul><ul><ul><ul><li>Traffic control [email_address] </li></ul></ul></ul><ul><ul><ul><li>Game playing [email_address] </li></ul></ul></ul><ul><ul><ul><li>Agent architectures [email_address] </li></ul></ul></ul><ul><ul><ul><li>Artificial AI, Trust [email_address] </li></ul></ul></ul><ul><ul><ul><li>Automated deduction [email_address] , [email_address] , [email_address] </li></ul></ul></ul><ul><ul><ul><li>Satisfiability [email_address] , [email_address] , [email_address] </li></ul></ul></ul><ul><ul><ul><li>Security protocol verification [email_address] </li></ul></ul></ul><ul><ul><ul><li>If you like theory [email_address] </li></ul></ul></ul><ul><li>Apply for a summer scholarship with us ! </li></ul>

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