online interactive problem-solving venkateshwar rao thota constraint systems laboratory
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Online Interactive Problem-Solving Venkateshwar Rao Thota Constraint Systems Laboratory University of Nebraska-Lincoln. Outline. Strategies for problem solving GTAAP Interactive system Conclusions & future work. Strategies for problem solving. Batch processing - PowerPoint PPT PresentationTRANSCRIPT
04/19/231
Constraint Systems Laboratory
Thota: MS Project defense
Online Interactive Problem-Solving
Venkateshwar Rao ThotaConstraint Systems Laboratory
University of Nebraska-Lincoln
04/19/232
Constraint Systems Laboratory
Thota: MS Project defense
Outline
• Strategies for problem solving
• GTAAP
• Interactive system
• Conclusions & future work
04/19/233
Constraint Systems Laboratory
Thota: MS Project defense
Strategies for problem solving
1. Batch processing Executing a series of non-interactive jobs all
at one time
2. Interactive problem-solving Interactive applications respond to
commands as the user enters them Computer and user work side-by-side to
define, analyze, and solve a problem
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Constraint Systems Laboratory
Thota: MS Project defense
Steps in batch processing• Analyze the problem
• Build a model
• Choose a solver and post problem instance
• Wait until a solution is found or a termination condition is reached
Build a model
Exit
Start
Problem instance
Yes
No
Run solver
Output solution
Is Solution found?
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Constraint Systems Laboratory
Thota: MS Project defense
Deficiencies of batch processing• Search algorithms may not terminate in a
reasonable amount of time• Constraints may not be amenable to formal
modeling Political correctness Preferences may vary with time and users
• User Cannot control search (its focus, progress) Cannot provide hints to break ties, balance tradeoffs Cannot modify/adapt problem encoding during
processing without restarting from scratch
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Constraint Systems Laboratory
Thota: MS Project defense
Advantages of interactive processing
• User has direct control of decisions being made can enforce online personal preferences and
alternative conditions
• Goal: exploit The processing power of computers and their
ability of maintaining consistency The human's domain expertise and intuition
for creative solving abilities
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Constraint Systems Laboratory
Thota: MS Project defense
Steps in interactive problem solving
• Build a model
• Post model to the interactive solver, which removes inconsistencies
• User offers hints, makes or removes decisions
• Update model and re-post to interactive solver
Is Solution found?
Obtain user hints
Interactive solver
Start
Problem instance
No
Exit
Reformulate problem
Build a model
Yes Output solution
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Constraint Systems Laboratory
Thota: MS Project defense
Outline
• Strategies for problem solving
• GTAAP: Background, problem modeling, system
architecture
• Interactive system
• Conclusions & future work
04/19/239
Constraint Systems Laboratory
Thota: MS Project defense
GTAAP• Given
A set of academic tasks A set of GTAs to assign to these tasks A set constraints restricting combinations
• Find a consistent & satisfactory assignment Consistent: assignment breaks no (hard)
constraints Satisfactory: assignment maximizes
1. number of courses covered 2. happiness of the GTAs
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Constraint Systems Laboratory
Thota: MS Project defense
GTAAP as a CSP• Variables
Courses involving grading, conducting lectures, labs & recitations
• Values GTAs + preference for each course (variable).
• Constraints Unary:
• ITA-certification, enrollment, time conflict, zero preferences, etc. Binary:
• Equality: Courses should have same GTAs• Mutex: Courses should have different GTAs (overlapping)
Non-binary: • Same-TA, capacity, confinement constraint
04/19/2311
Constraint Systems Laboratory
Thota: MS Project defense
GTAAP: System architectureServer Environment (cse.unl.edu)
Manager Web-interface
Database (GTAs
& Courses)
Hire GTAs
Student Web-interface
Profile Information
Course Information
Setup courses
Client
Student
Manager
Interactive system
Interactive SolverInteractive selections
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Constraint Systems Laboratory
Thota: MS Project defense
Outline• Strategies for problem solving • GTAAP• Interactive system
Motivation & requirements Components
I. Visual interfacesII. AlgorithmsIII. Database
• Conclusions & future work
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Constraint Systems Laboratory
Thota: MS Project defense
Difficulties of GTAAP• Manual solving
Too many constraints: Tedious & error prone Unsatisfactory assignments Difficult to test alternative assignments
• Automated solvers BT [Glaubius], LS [Zou], ERA [Zou], RDGR [Guddeti]
Large search space Often over-constrained (problematic for
incomplete solvers)
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Constraint Systems Laboratory
Thota: MS Project defense
Interactive processing
General requirementsI. A visual interface for user interaction
II. An algorithm that Accounts for user’s input and integrates it into the
problem encoding Propagates the effect of the decision to prepare the new
encoding for another input from user
III. A database to store Problem data (perhaps, also intermediate encodings) Alternative partial or complete solutions
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Constraint Systems Laboratory
Thota: MS Project defense
I. Visual interface• Interface
Offers dual perspective: Course & GTA-centered Shows legal choices:
• possible (blue)• un-available (pink)
Sorts legal choices by decreasing preference and shows available capacity
• User actions Make an assignment (GTA to course, course to GTA) Undo an assignment
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Constraint Systems Laboratory
Thota: MS Project defense
Dual perspectiveCourse-centered view GTA-centered view
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Constraint Systems Laboratory
Thota: MS Project defense
Course-centered view
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Constraint Systems Laboratory
Thota: MS Project defense
Consistent GTAs List of available GTAs for assignment
Inconsistent GTAsList of busy GTAs who cannot be assigned
GTA Preference for courseGTA name
Available GTA capacity
Course number, section
Course name Course load
Course timings and days
Assigned GTA
Course-centered view
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Constraint Systems Laboratory
Thota: MS Project defense
GTA-centered view
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Constraint Systems Laboratory
Thota: MS Project defense
GTA-centered view
Courses that are available for assignment to GTA
Courses that cannot be assigned
GTA name
Advisor Courses assigned to GTA
Speak test, ITA qualification, GTA capacity
Course name
GTA Preference for courseCourse number – section
Course load
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Constraint Systems Laboratory
Thota: MS Project defense
Visual interface: Other features
Sorting functionality
• Show / Hide displayed attributes
• Each attribute has an accessor method for controlling its display, thus allowing easy addition and removal of new attributes
• Sorting according to an attribute
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Constraint Systems Laboratory
Thota: MS Project defense
Visual interface: summary
• An online web-based interface, available anytime & anywhere
• Intuitive and easy to use
• Offers a flexible dual perspective
• Allows user to undo decisions
• Instantly displays consequences of actions
• Provides a sorting functionality for displaying results
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Constraint Systems Laboratory
Thota: MS Project defense
II. Algorithms for interactivity• Algorithms: efficient algorithms for maintaining
problem consistency Node-Consistency (NC) algorithm Arc-Consistency (AC) algorithm Propagation of available capacity (arc-
consistency on a global constraint)
• Functionalities Making / undoing assignments Propagating effect of modifications, including
capacity
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Constraint Systems Laboratory
Thota: MS Project defense
The algorithms in the systemServer environment
Client
Manager (browser)
Web-Server
Interactive selections web-
interface (PHP scripts)
TCP/IP connection Function access
Interactive Solver (LISP based daemon process)
SocketListener
CSP model GTAAP structures
Consistency Algorithms
LISP command prompt
Port file
Database connection
MySQLDatabase
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Constraint Systems Laboratory
Thota: MS Project defense
Basic consistency algorithms
• Node-consistency (NC) Goes through each course Ensures that all listed GTAs are legal
• Arc-consistency (AC) Goes through every binary constraint Ensures that a GTA x is listed for one course only when there is
another GTA y listed for the other course consistent with x given the binary constraint between the two courses, otherwise it removes GTA x
• Propagating capacity constraint Goes through all courses requiring a given GTA Ensures that the course load does not exceed the GTA’s capacity
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Constraint Systems Laboratory
Thota: MS Project defense
Assignment
1. Assign GTA to course
2. Update capacity
3. Update domains
4. Propagate capacity
5. Perform AC
Set course TA g
Exit
Start
Perform arc-consistency
For all unassigned courses,Propagate capacity of g
Domain(c) {g}
Capacity(g) Capacity(g) - Load(c)
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Constraint Systems Laboratory
Thota: MS Project defense
Unassignment
1. Remove the course assignment
2. Update capacity3. Do all the previous
assignments and propagate the capacity
4. Run AC
Unassign g from c
Exit
Start
Store current assignments
Reset all course domains
Capacity(g) = Capacity(g) + Load(c)
For each stored assignment <c’,g’>1.Set course TA (c’) g’2.Domain(c’) {g’}3.Propagate capacity of g’
Perform arc-consistency
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Constraint Systems Laboratory
Thota: MS Project defense
III.DataBase
• Change problem definition [Lim]
• Save / retrieve scenario
• All the saved scenarios will be lost when data is re-fetched.
Saved scenarios
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Constraint Systems Laboratory
Thota: MS Project defense
Outline
• Strategies for problem solving
• GTAAP
• Interactive system
• Conclusions & future work
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Constraint Systems Laboratory
Thota: MS Project defense
Implementation of interactivity
• The visual interface in PHP
• The propagation algorithms in LISP
• The database in MySQL [Lim] We added 2 tables for storing alternative
solutions
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Constraint Systems Laboratory
Thota: MS Project defense
Final note• Conclusions
Intuitive, facilitates problem solving Helps manager assessing needed resources Supports the quick development of ‘stable’ solutions
• Future work Comparison and combination of partial solutions Cooperative, hybrid search Visualization of solution space
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Constraint Systems Laboratory
Thota: MS Project defense
Thank you for your attention
I welcome your questions…