1. Research Information Session Associate Professor John Thornton Gold Coast BIT Honours Degree Convenor J.Thornton@griffith.edu.au

2. John Thornton About the Honours Degree q Must have GPA of 5.0 (credit) or better for 2 nd and 3 rd year of Bachelor degree q One year full-time – 80CP made up of: q 40CP Dissertation q 10CP Research Methods in IT + 30CP electives  Up to one 3 rd year course  Rest must be honours level  Supervisor may run a special subject q Graded 1 st, 2.1, 2.2 or 3 rd class

3. John Thornton Choosing a Research Topic q Honours is about research training q Find a topic that interests you q Find a supervisor you can work with q Consider your future after honours  Entry into a more interesting job?  Research Higher Degree, e.g. PhD?  Career as an academic? q Your choice of topic and supervisor sets the direction of your future life – consider carefully – getting 1 st class also matters

4. John Thornton Financial Support q University values its research students q Your work and publications raise the university’s profile q Various School Scholarships:  Summer Project $2,000  Honours Scholarship $2,000 q Other sources:  IIIS, NICTA, Supervisor Funds  Tutoring opportunities

5. John Thornton How To Apply q Closing Date for applications 31 st October q For details of how to apply, see: http://www.griffith.edu.au//ua/aa/sta/admission/honours/ q For details of the degree structure, see: http://www17.griffith.edu.au/cis/p_cat/admission.asp?ProgCode=2020&type=overview

6. John Thornton Research with Dr John q Gold Coast Honours Convenor q Associate Director IIIS for Gold Coast q RHD Coordinator IIIS and ICT Gold Coast q NICTA researcher q Leader of Constraint Satisfaction and Hierarchical Temporal Memory research groups  8 PhD completions  1 MPhil, 2 Masters, 5 Honours completions

7. John Thornton What are Constraints? A constraint is a relationship over object(s) in the world. What is allowed? What is not allowed? Knowledge about broad range of real world domains can be easily expressed in terms of constraints

8. John Thornton Constraint Programming “Constraint programming represents one of the closest approaches computer science has yet made to the Holy Grail of programming: the user states the problem, the computer solves it.” Eugene Freuder, Constraints, April, 1997.

9. John Thornton Constraint Satisfaction Given: q A set of variables q A set of permitted values for each variable q A set of constraints on subsets of variables Find: an assignment of values to variables such that all the constraints are satisfied.

10. John Thornton Example: Graph Colouring  Variables: geometric regions (e.g., all states in India)  Domain: available colours  Constraints: neighbours cannot have the same colour

11. John Thornton General Techniques q Problems are often NP-complete q Over-constrained q Two classes of technique:  Backtracking  Local search

12. John Thornton Eight Queen Problem Variable Constraint Domain

13. John Thornton Local Search Place 8 Queens randomly on the board Pick a Queen: Calculate cost of each move 31054111 Take least cost move then try another Queen 04441111 434111131 333211112 344111132 223422212 323211231 204212231 232213231 233212221 232322131 223213211 322333301 Answer Found

14. John Thornton Selected Results q Building Structure into Local Search for SAT  IJCAI’07 Distinguished Paper Award q Winner of SAT Competition Gold Medals  gNovelty+ (2007), R+AdaptNovelty+ (2005) q Temporal Reasoning  Local Search (JLC), New SAT encoding (CP’06) q Hybrid Search  Resolution + SLS (AAAI’05) q Evolving Algorithms for CSPs  Genetic programming (CEC’04, PRICAI’04)

15. John Thornton Research Challenges q Parameter free clause weighting local search ( for SAT competition ) q Exploiting structure ( dependency lattice ) q Local search method for UNSAT problems ( IJCAI’97 challenge problem ) q Methods for solving problems in non – CNF form ( bio-informatics, model checking ) q Handling over-constrained problems q Transforming complex problems into CSPs/SAT

16. John Thornton New Research Directions q Hierarchical Temporal Memory  Using insights from computational neuroscience to build more robust and flexible pattern recognition machines  Exploiting temporal connections between inputs (temporal pooling)  Combining recognition with prediction

17. John Thornton The Teams IIIS CSP/SAT: Abdul Sattar, Wayne Pullan, Duc Nghia Pham, Stuart Bain, Lingzhong Zhou, Matthew Beaumont, Valnir Ferreira Jr. Abdelraouf Ishtaiwi NICTA CSP/SAT : Michael Maher, Charles Gretton, John Slaney, Anbulagan IIIS HTM: Michael Blumenstein, Trevor Hine, Jolon Faichney, Richard Speter

18. John Thornton Thank You Questions? (also see www.cit.gu.edu/~johnt/)