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Course Scheduling Software Progress Presentation December 22, 2004

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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  1. Course Scheduling SoftwareProgress Presentation December 22, 2004

  2. 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

  3. 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

  4. Project Status Presented by Glen Winston

  5. Project Status

  6. 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

  7. 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.

  8. Risk Update Presented by Steve Moran

  9. 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

  10. Component Overview Presented by Glen Winston

  11. Architectural Overview

  12. GUI Walkthrough Presented by Dee Dee Ives

  13. Model Overview Presented by Glen Winston

  14. Model Overview

  15. Scheduling Proof of Concept: Project Goal Presented by Glen Winston

  16. 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.

  17. Scheduling Proof of Concept: Approach Presented by Steve Moran

  18. 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.

  19. 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

  20. 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

  21. 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

  22. 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>

  23. 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

  24. 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>

  25. 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

  26. Scheduling Proof of Concept: Challenges Presented by Bob McKeever

  27. 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.

  28. Constraint Programming Solutions • Backtracking. • Backtracking with Forward Checking. • Backtracking with Forward Checking and Heuristics. • Tic, Tac, Toe as an example

  29. 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.

  30. Drools Negatives (Con’t) • Team members have no experience with Drools programming. • 3 Team members have no experience with programming expert systems.

  31. 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?

  32. 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.

  33. Scheduling Proof of Concept: Drools Sample Presented by Bob McKeever

  34. <?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>

  35. <!-- 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>

  36. <!-- 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>

  37. Scheduling API Presented by Bob McKeever

  38. Scheduling API

  39. Deployment Plan Presented by Bob McKeever

  40. 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.

  41. 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!

  42. 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.)

  43. Q & A

  44. Backward & Forward Chaining Presented by Steve Moran

  45. 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

  46. 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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