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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.
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