1. ERA on an over-constrained problem A Constraint-Based System for Hiring & Managing Graduate Teaching Assistants Ryan Lim, Praveen Venkata Guddeti, and B.Y. Choueiry Constraint Systems Laboratory Computer Science & Engineering University of Nebraska-Lincoln Contributions  A constraint-based model for the Graduate Teaching Assistants Assignment Problem (GTAAP)  A working prototype system for data acquisition & interactive problem solving  A uniform platform for the design, comparison & characterization of search techniques (so far, BT, LS, ERA, randomized BT) Future research directions  On-line hybridization of cooperative search techniques  Compact representation & visualization of solutions Impact & benefits  Progress in characterizing behavior of search  Education & training in Constraint Processing  Service to the department (e.g., decreased time & effort for finding a solution, reduced the number of assignment conflicts & modifications, improved matching of GTAs to classes, etc.) Project Summary Interactive Decision Making Backtrack search Theoretically complete, not in practice Multi-Agent Search Bottleneck visualization [1] Glaubius. A Constraint Processing Approach to Assigning Graduate Teaching Assistants to Courses. Undergraduate Honors Thesis. CSE-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, page 789, 2002. [5] Guddeti & Choueiry. An Empirical Study of a New Restart Strategy for Randomized Backtrack Search, Workshop on CSP Techniques with Immediate Applications (CP 04), 2004. [6] Lim, Guddeti & Choueiry. An Interactive System for Hiring and Managing Graduate Teaching Assistants. PAIS/ECAI 04, 2004. [7] Zou. Iterative Improvement Techniques for Solving Tight Constraint Satisfaction Problems. Masters thesis, CSE-UNL, 2003. [8] 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. Support: NSF grant #EPS-0091900, Department of Computer Science & Engineering (UNL) & Constraint Systems Laboratory. Experiments were carried out on PrairieFire, courtesy of the Research Computing Facility of Computer Science & Engineering. Randomized BT with Restarts References Summary ERA on a solvable problem  Solves tight GTAAP instances unsolved by all other techniques  Deadlock in over-constrained problems –Undermines stability & results in short solutions –Useful to isolate & represent conflicts in a compact manner Modeling CONSTRAINT INTENSION EXTENSION MUTEX CONFINEMENT EQUALITY CAPACITY DIFFTA DEFICIT CERTIFICATION OVERLAP NILPREF TAKING-COURSE Constraint model  Variables: Grading, lectures, labs & recitations  Values: GTAs (+ preferences)  Constraints: Unary, binary, global (capacity)  Objective 1.Longest consistent solution 2.That maximizes preferences Given  A set of GTAs  A set of courses  A set of constraints that specify allowable assignments Find a consistent & satisfactory assignment Consistent: breaks no constraint Satisfactory: maximizes 1.number of courses covered 2.happiness of the GTAs Task-centered view Resource-centered view September 23, 2004 Data set 1 (69 variables, over-constrained) CPU run time30 sec5 min30 min1 hour6 hours24 hours Shallowest BT level545352 51 Longest solution57 Geometric mean of preference values2.152.17 2.212.27 Thrashing in large search spaces 24 hr: 51 (26%) 1 min: 55 (20%) Max depth: 57 Depth of tree: 69 by BT after... Shallowest level reached Huge branching factor causes thrashing and backtrack never reaches early variables  Solvable instances: ERA > RDGR > RGR > BT > LS  Over-constrained instances: RDGR > RGR > BT > LS > ERA  At phase transition: RDGR > RGR > BT > ERA > LS  We improve RGR of [Walsh 99] into RDGR  Cutoff values in RDGR grows slower than in RGG, thus allowing more frequent restarts Effect of running time Under/over constrained problems At phase transitionOutside phase transition  Design ‘progress-aware’ restart strategies (cutoff value chosen during search)  Design new hybrid, cooperative search strategies Current Investigations System Architecture 1.Interface for GTA applications 2.Interface for Manager:  View/edit GTA records  Setup classes  Specify constraints  Drive problem solving 3.A local relational database 4.Interactive & automated search Password Protected Access for GTAs Cooperative, hybrid Search Strategies Other structured, semi-structured, or unstructured DBs In progress Password Protected Access for Manager Visualization widgets Local DB Interactive Search Automated Search Heuristic BT Stochastic LS Multi-agent Search Randomized BT