1. Maintaining Consistency in Resource Allocation Trevor Janke & Berthe Y. Choueiry Goals  Study & refine the constraint model of the existing Graduate TAs Assignment Problem (GTAAP) prototype  Improve  Interactive problem-solving functionalities  Data acquisition processes  Database design  Interfaces Impact & benefits  Education & research in Constraint Processing  Service to CSE  Decreased time & effort for building an assignment  Reduced the number of conflicts & modifications  Improved matching of GTAs to classes  Maintains historical data: TA training & performance Project Summary Current System [1] Glaubius. A Constraint Processing Approach to Assigning Graduate Teaching Assistants to Courses. Undergraduate Honors Thesis, UNL, 2001. [2] Glaubius & Choueiry. Constraint Modeling and Reformulation in the Context of Academic Task Assignment. Workshop on Modeling and Solving Problems with Constraints (ECAI 2002), 2002. [4] Glaubius & Choueiry. Constraint Modeling in the Context of Academic Task Assignment. CP 02, LNCS 2470, 2002. [5] Zou. Iterative Improvement Techniques for Solving Tight Constraint Satisfaction Problems. MS thesis, UNL, 2003. [6] Zou & Choueiry. Characterizing the Behavior of a Multi-Agent Search by Using it to Solve a Tight, Real-World Resource Allocation Problem. Workshop on Applications of Constraint Programming (CP 03), pages 81—101, 2003. [7] Guddeti & Choueiry. An Empirical Study of a New Restart Strategy for Randomized Backtrack Search. Workshop on CSP Techniques with Immediate Applications (CP 04), 2004. [8] Lim, Guddeti & Choueiry. An Interactive System for Hiring and Managing Graduate Teaching Assistants. Prestigious Applications of Artificial Intelligence/European Conference on Artificial Intelligence, pages 730—734, 2004. [9] Guddeti. An Improved Restart Strategy for Randomized Backtrack Search. MS thesis, UNL, 2004. [10] Lim. GTAAP: An Online System For Managing and Assigning Graduate Teaching Assistants to Academic Tasks. MS Project, UNL, 2006. References Problem & Constraint Model CONSTRAINT INTENSION EXTENSION MUTEX CONFINEMENT EQUALITY CAPACITY DIFFTA DEFICIT CERTIFICATION OVERLAP NILPREF TAKING-COURSE Constraint model  Variables represent tasks: Grading, lectures, labs, recitations  Values represent resources: GTAs  Constraints: Qualitative & quantitative relations  Objective functions 1.Longest consistent solution 2.Maximize preferences (maxmin, geometric mean, etc.) Given  A set of GTAs: Abilities, availability (schedule & capacity), preferences  A set of courses: Size (load), needs (grading, recitation, lab)  A set of constraints specifying allowable assignments Find a consistent & satisfactory assignment of TAs to courses Consistent: breaks no constraint Satisfactory: maximizes the… 1.Number of courses serviced 2.Preferences of the GTAs System Architecture Web-based system  Password protected  Accessible to admitted foreign students TAs’ interface  Academic record  Qualifications  Capacity & preferences Secure Access for GTAs Other structured, semi-structured, or unstructured Databases Secure Access for Manager Visualization widgets Local Database Interactive & Automatic Search Supporting Interactive Decision Making Manager’s interface  View/edit GTA records  Setup classes  Specify constraints  Drive problem solving Instructors’ interface  Evaluate TAs Use Excel to  Keep track of classes  Show assignments of students to classes  Keep track of students Opportunities for improvement  Data is entered manually in Excel sheets  TAs capacity vs. course load is checked manually  Scalability is a major concern New system maintains a consistent dual view of decisions For each class, the interface shows all suitable TAs: both those available & those busy For each TA, the interface displays all courses for which the TA  is adequate  is already assigned Secure Access for Instructors  still has some available capacity  no longer has sufficient capacity, etc.