Honours Information Session

John Thornton Gold Coast BIT Honours Degree Convenor [email protected]

About the Honours Degree

     Must have GPA of 5.0 (credit) or better for 2 nd and 3 rd year of Bachelor degree One year full-time – 80CP made up of: 40CP Dissertation 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 Graded 1 st , 2.1, 2.2 or 3 rd class John Thornton

Choosing a Research Topic

     Honours is about research training Find a topic that interests you Find a supervisor you can work with Consider your future after honours    Entry into a more interesting job?

Research Higher Degree, e.g. PhD?

Career as an academic?

Your choice of topic and supervisor sets the direction of your future life – consider carefully – getting 1 st class also matters John Thornton

Financial Support

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

How To Apply

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

Research with Dr John

     Gold Coast Honours Convenor Associate Director IIIS for Gold Coast RHD Coordinator of the School of ICT NICTA researcher Leader of Constraint Satisfaction and Hierarchical Temporal Memory research groups   8 PhD completions 1 MPhil, 2 Masters, 5 Honours completions 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 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.

John Thornton

Constraint Satisfaction

Given:  A set of variables  A set of permitted values for each variable  A set of constraints on subsets of variables Find: an assignment of values to variables such that all the constraints are satisfied .

John Thornton

Example: Graph Colouring

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

Example: SAT Problem

Variables: propositional variables Domain: truth values {True, False} Constraints: Clausal Normal Form (a propositional formula): (x1  x2   x3)  (  x2   x3  x4)  ( x1  x3  x4) John Thornton

General Techniques

   Problems are often NP-complete Over-constrained Two classes of technique:   Backtracking Local search John Thornton

Eight Queens Problem

Domain Variable Constraint John Thornton

Local Search

2 3 1 0 1 4 1 1 4 1 4 2 1 1 1 5 1 4 3 1 4 1 2 0 1 4 3 1 3 1 2 2 1 2 3 2 1 John Thornton

Selected Results

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

Research Challenges

      Parameter free clause weighting local search ( for SAT competition ) Exploiting structure ( dependency lattice ) Local search method for UNSAT problems ( IJCAI’97 challenge problem ) Methods for solving problems in non – CNF form ( bio-informatics, model checking ) Handling over-constrained problems Local Search Invariance Engine for NICTA’s ATOMIC project John Thornton

New Research Directions

 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 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, Andrew Verden, Mark Wallace, Peter Stuckey

IIIS HTM:

Michael Blumenstein, Trevor Hine, Jolon Faichney John Thornton

Thank You Questions?

(also see www.cit.gu.edu/~johnt/ )

John Thornton