online interactive problem-solving venkateshwar rao thota constraint systems laboratory

04/19/231

Constraint Systems Laboratory

Thota: MS Project defense

Online Interactive Problem-Solving

Venkateshwar Rao ThotaConstraint Systems Laboratory

University of Nebraska-Lincoln

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Outline

• Strategies for problem solving

• GTAAP

• Interactive system

• Conclusions & future work

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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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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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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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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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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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Outline


• GTAAP: Background, problem modeling, system

architecture



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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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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

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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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Outline• Strategies for problem solving • GTAAP• Interactive system

Motivation & requirements Components

I. Visual interfacesII. AlgorithmsIII. Database


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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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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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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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Dual perspectiveCourse-centered view GTA-centered view

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Course-centered view

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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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GTA-centered view

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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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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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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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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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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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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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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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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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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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Outline


• GTAAP



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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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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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Thank you for your attention

I welcome your questions…

online interactive problem-solving venkateshwar rao thota constraint systems laboratory

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