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Course Scheduling Software Progress Presentation December 22, 2004. Customer: Jed Lippard, Upper School Director, Prospect Hill Academy Charter School Team Members dev@scheduler.tigris.org Glen Winston Robert McKeever Steve Moran Valdeva Ives. Agenda. Project Status Risk Update

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Course Scheduling Software Progress Presentation December 22, 2004

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Course Scheduling SoftwareProgress Presentation December 22, 2004


Customer:

Jed Lippard, Upper School Director,

Prospect Hill Academy Charter School

Team Members

dev@scheduler.tigris.org

Glen Winston

Robert McKeever

Steve Moran

Valdeva Ives


Agenda

  • Project Status

  • Risk Update

  • Architectural Overview

  • GUI Walkthrough

  • Model Overview

  • Scheduling

    • Proof of Concept

    • Constraint Programming

    • Technology Alternatives

    • Drools pros & cons

    • Drools Example

    • Scheduling API

  • Deployment Plan


Project Status

Presented by

Glen Winston


Project Status


Process Overview

  • Project Phases: Fall

    • Initiation (100% complete)

    • Analysis (100% complete)

    • Functional Design (100% complete)

    • Technical Design (70% complete)

  • Project Phases: Spring

    • Completion of Technical Design

    • Development

    • Testing

    • Deployment


Process Overview: Technical Design

  • Key Deliverable of this phase

    • Technical Specifications Document

      • Document all non-trivial classes in the system.

      • Informative sequence diagrams of key controllers in the system.

      • Document and prove algorithm for scheduling classes.


Risk Update

Presented by

Steve Moran


Risks

  • Scheduling technology implementation – higher

    • Advancing Rule Base during semester break

    • Continuing Research into Production Systems & Constraints

    • Assigning 50% of team to task

  • Scheduling technology choice – moderate

    • Several well understood alternatives available

  • Post-Deployment Maintenance & Support – low to moderate

    • Specification biased toward currently stated requirements

    • Will rely on actual implementation

  • Added Requirement to schedule individual students – lower

    • Students primarily move in groups

    • Believe less involved than class scheduling

  • Feature creep – lower

    • Detailed documentation & customer contact

  • Insufficient time - lower

    • Following regimented process


Component Overview

Presented by

Glen Winston


Architectural Overview


GUI Walkthrough

Presented by

Dee Dee Ives


Model Overview

Presented by

Glen Winston


Model Overview


Scheduling Proof of Concept: Project Goal

Presented by

Glen Winston


Scheduling Proof of Concept: Project Goal

Scheduler Component is highest area of risk in technical design.

Risk Mitigation Plan

  • Proof of concept

  • Two exclusive resources

  • Hand written algorithm fallback plan

    Proof of Concept is complete, we learned

    •We were able to create periods in a rules engine.

    •We were able to fill periods in a rules engine.


Scheduling Proof of Concept: Approach

Presented by

Steve Moran


Scheduling Problem To Solve

  • Students, Teachers, & Subjects,

  • in 7 grades, subdivided into cohorts (groups),

  • into classrooms of various sizes & locations,

  • with 7 daily schedule blocks,

  • with a rotating class schedule,

  • with 81 initial constraints.


Technology Alternatives

  • DROOLS – actively being prototyped

    • “understandable” XML style rules in java

  • JESS – capable, but licensing issues

  • CLIPS – “lisp style” rules implemented in C

  • JClips – directly runs CLIPS files in java

    • testbed for existing CLIPS.clp example files

  • Prolog – inefficient backward chaining

  • Brute force - inefficient backward chaining


Rete-based Inference Engine

  • Declarative programming – “what is”

  • Forward chaining rules – “data driven”

  • Fast in-memory network

    • “The only algorithm for implementing production rules whose performance is demonstrably independent of the number of rules.”

  • Rules can change without recompiling


Facts

Rules

Working Memory

Classes

Blocks

<parameter>

Domain

<condition>

Boolean

<consequence>

java

Rooms

Assert

Facts

Rules

Retract

Modify

Collections of Objects

Ordered by Salience

ReteAlgorithm

In a nutshell, we want to schedule Classes into Rooms with Blocks


Before Rule Fires

After Rule Fires

222

222

101

101

Working

Memory

Rooms

Rooms

222 - 8AM

222 - 9AM

101 - 8AM

222 - 10AM

101 - 9AM

101 - 10AM

Blocks

<parameter> Room </parameter>

<condition> none </condition>

<consequence>

assert(new Block(room.num,8))

assert(new Block(room.num, 9))

assert(new Block(room.num, 10))

</consequence>


room 101 has block at 8

room 101 has block at 9

room 101 has block at 10

room 222 has block at 8

room 222 has block at 9

room 222 has block at 10


Working Memory

222 - 8AM

222

222 - 9AM

Math

101 - 8AM

222 - 10AM

101

English

101 - 9AM

101 - 10AM

Rooms

Blocks

Class

<consequence>

class.isScheduled = true

modify(class)

block.class = class.id

modify(block)

</consequence>

<parameter>

Room

Block

Class

</parameter>

<condition>

block.class = null

class.numStudents < room.capacity

class.isScheduled == false

</condition>


Scheduling class: 5 in room: 101 at: 8

Scheduling class: 15 in room: 101 at: 9

Scheduling class: 6 in room: 101 at: 10

Scheduling class: 8 in room: 222 at: 8

Scheduling class: 16 in room: 222 at: 9

Scheduling class: 20 in room: 222 at: 10


Scheduling Proof of Concept: Challenges

Presented by

Bob McKeever


Constraint Programming Problem

  • Scheduling is an NP Complete Problem.

  • Requires polynomial time to solve.

  • Could be solved trivially by using a systemic search.

  • Generate and test until a solution is found.


Constraint Programming Solutions

  • Backtracking.

  • Backtracking with Forward Checking.

  • Backtracking with Forward Checking and Heuristics.

  • Tic, Tac, Toe as an example


Drools Negatives

  • Very little documentation.

  • Does not have all the same features as CLIPS. (At present “not” is not supported.)

  • Can not directly convert from CLIPS code to Drools code.

  • Small user community.

  • Just out of Beta.


Drools Negatives (Con’t)

  • Team members have no experience with Drools programming.

  • 3 Team members have no experience with programming expert systems.


Drools Positives

  • Handles the constraints well. Much better than nested if statements.

  • Open source.

  • Seems to have a lot of “buzz”.

  • Did I mention it was free?


Drools Positives (Con’t)

  • We are starting to get up to speed with it. Now have some working examples.

  • May be able to post our work as an example on their web site to have others carry on. May help on maintenance issues.


Scheduling Proof of Concept: Drools Sample

Presented by

Bob McKeever


<?xml version="1.0" encoding="UTF-8"?>

<!--

The definition of a RuleExecutionSet is not within the scope of the JSR 94.

The implementation given in this file is written for the reference

implementation. A rule engine vendor verifying their rule engine should

modify this file to their specific needs.

-->

<rule-set name="Scheduler"

xmlns="http://drools.org/rules"

xmlns:java="http://drools.org/semantics/java"

xmlns:xs="http://www.w3.org/2001/XMLSchema-instance"

xs:schemaLocation="http://drools.org/rules rules.xsd

http://drools.org/semantics/java java.xsd">

<java:import>java.util.*</java:import>

<java:import>org.drools.examples.schedule.model.Block</java:import>

<java:import>org.drools.examples.schedule.model.ClassInfo</java:import>

<java:import>org.drools.examples.schedule.model.ClassToSchedule</java:import>

<java:import>org.drools.examples.schedule.model.Room</java:import>

<java:import>org.drools.examples.schedule.model.RoomCourseRelation</java:import>

<java:import>org.drools.examples.schedule.model.RoomInfo</java:import>

<java:import>org.drools.examples.schedule.model.SchoolClass</java:import>


<!--

Create the blocks

-->

<rule name="generate blocks" salience="40">

<parameter identifier="roomInfo">

<class>RoomInfo</class>

</parameter>

<java:consequence>

System.out.println("Making block " + roomInfo.number);

drools.assertObject(new Block(roomInfo.number, 8));

drools.assertObject(new Block(roomInfo.number, 9));

drools.assertObject(new Block(roomInfo.number, 10));

</java:consequence>

</rule>


<!--

Schedule.

-->

<rule name="schedule" >

<parameter identifier="roomInfo"> <class>RoomInfo</class> </parameter>

<parameter identifier="block"> <class>Block</class> </parameter>

<parameter identifier="classInfo"> <class>ClassInfo</class> </parameter>

<java:condition> block.schoolClass == 0 </java:condition>

<java:condition> classInfo.numStudents &lt;= roomInfo.capacity </java:condition>

<java:condition> classInfo.isScheduled == false </java:condition>

<java:consequence>

classInfo.isScheduled = true;

drools.modifyObject(classInfo);

block.schoolClass = classInfo.id;

drools.modifyObject(block);

System.out.println("Scheduling class: " + block.schoolClass +

" in room: " + block.room + " at: " + block.time);

</java:consequence>

</rule>

</rule-set>


Scheduling API

Presented by

Bob McKeever


Scheduling API


Deployment Plan

Presented by

Bob McKeever


Deployment Plan Goals

  • Keep customer informed.

  • Get buy in from customer’s IT administrator.

  • Create Windows executable.

  • Provide physical program to the customer.

  • Provide documentation to the customer.

  • Give the program the Windows look and feel.


Deployment Plan Actions

  • Use launch4j to create a Windows executable with Splash screens and icons.

  • Meet with customer’s IT administrator. Request computer that has been backed up.

  • Create set up program using Wise for install and uninstall. Burn onto a CD.

  • Test!, Test!, Test!


Deployment Plan Actions (Con’t)

  • Develop documentation and detailed installation instructions.

  • Provide professional documentation and CD.

  • Meet at Customers Site for installation.

  • Provide a Jar file version on the Web site of a generic scheduler. (One that does not have the customer’s logos on it and is not dependent on Windows to run.)


Q & A


Backward & Forward Chaining

Presented by

Steve Moran


Two Approaches

  • Backward Chaining

    • Imperative based systems – how to

    • Queries fact space for goal ‘truth’

    • Mechanism used in most most logic programming, i.e. Prolog

  • Forward Chaining

    • Declarative based systems – what is

    • Triggered on fact space information

    • A data driven technique to reach inferences from a set of facts


Backward vs. Forward Chaining

  • Backward-chaining means that no rules are fired upon assertion of new knowledge. When an unknown piece of knowledge is detected all rules relevant to the knowledge in question are fired until the question is answered, if possible. Thus, backward chaining systems normally work from a goal state back to the original state.

  • Forward-chaining implies that upon assertion of new knowledge, all relevant rules are fired exhaustively, effectively making all knowledge about the current state explicit within the state. Forward chaining may be regarded as progress from a known state (the original knowledge) towards a goal state(s).

  • The branching factor (the number of considerations at each state) may vary between forward and backward chaining and thus determine which method is most efficient.

Source: http://ai.eecs.umich.edu/cogarch0/index.html


Diagnosis

Patient

Source: http://www.cise.ufl.edu/class/cap6685sp03/Lectures/ES-CH4b.pdf


Backward vs. Forward Chaining

  • Backward-chaining means that no rules are fired upon assertion of new knowledge. When an unknown piece of knowledge is detected all rules relevant to the knowledge in question are fired until the question is answered, if possible. Thus, backward chaining systems normally work from a goal state back to the original state.

  • Forward-chaining implies that upon assertion of new knowledge, all relevant rules are fired exhaustively, effectively making all knowledge about the current state explicit within the state. Forward chaining may be regarded as progress from a known state (the original knowledge) towards a goal state(s).

  • The branching factor (the number of considerations at each state) may vary between forward and backward chaining and thus determine which method is most efficient.

Source: http://ai.eecs.umich.edu/cogarch0/index.html


Source: http://www.cise.ufl.edu/class/cap6685sp03/Lectures/ES-CH4b.pdf


Source: http://www.cise.ufl.edu/class/cap6685sp03/Lectures/ES-CH4b.pdf


Source: http://www.cise.ufl.edu/class/cap6685sp03/Lectures/ES-CH4b.pdf


Backward vs. Forward Chaining

  • Backward-chaining means that no rules are fired upon assertion of new knowledge. When an unknown piece of knowledge is detected all rules relevant to the knowledge in question are fired until the question is answered, if possible. Thus, backward chaining systems normally work from a goal state back to the original state.

  • Forward-chaining implies that upon assertion of new knowledge, all relevant rules are fired exhaustively, effectively making all knowledge about the current state explicit within the state. Forward chaining may be regarded as progress from a known state (the original knowledge) towards a goal state(s).

  • The branching factor (the number of considerations at each state) may vary between forward and backward chaining and thus determine which method is most efficient.

Source: http://ai.eecs.umich.edu/cogarch0/index.html


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