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Phase 1 Problems

Phase 1 Problems. Small size 20 to 150 methods 10 to 20 agents Problem Classes One static + one dynamically arriving problem Uncertainty in duration/quality Too much work to do (only class with more than 70 methods) Non Local Effects (hard enables + soft facilitates) Syncronization. 1.

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Phase 1 Problems

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  1. Phase 1 Problems • Small size • 20 to 150 methods • 10 to 20 agents • Problem Classes • One static + one dynamically arriving problem • Uncertainty in duration/quality • Too much work to do (only class with more than 70 methods) • Non Local Effects (hard enables + soft facilitates) • Syncronization 1

  2. Phase 2 • Much more complex scenario generator • mix and match various problem classes, now called templates • better control of the assignment of agents to methods • within a template & between problems • More dynamics (both new tasks & changes to old) • New NLEs (hinder, disable) • New QAFs (Sum-And, Exactly-One) • Much larger problems • 10–100 agents • 725–10,000 methods 2

  3. Experimental Environment • Scenario Generator generates random scenarios. • Each scenario contains one or more problems. • Problems are independent, except that they may overlap in time. Agents can only do one thing at a time. • Problems consist of Template Instances. • Templates comprise tasks and methods. • Methods are individual executable actions.

  4. Experimental Environment • Each problem, and some complex templates, are broken into a sequential set of time windows. • Each window has an earliest start time and a deadline. • We can independently control how tight these start time and deadlines are relative to method execution time and how much windows overlap with one another. • Non-local effects (NLEs) also are limited to be from earlier windows to later windows.

  5. Templates • Simple • Static single-window problems from Phase 1 • Syncronization • Single sinch point from Phase 1 • Dynamic Simple • Dynamically arriving simple single-window • NLE Chain • Multiple windows chained together with enables or facilitates • Recursive template for each window 5

  6. Templates • Multi-Synch • Multiple sych points under a single window • Enables Tracks • Multiple “tracking” tasks, each with a series of linearly enabled subtasks • Contingency • multiple “preparation” methods enable multiple “completion” methods. • A crucial task with multiple outcomes determines what “completion” method is actually needed at runtime 6

  7. Templates • Second Chance Dynamic • “preparation” task almost impossible, enables “completion” task. • an “easier” preparation task arrives, allowing an easier second chance at completion • Circular Soft NLE • Soft NLEs (facilitates, hinders) are set up in cycles, creating a hard optimization problem 7

  8. Simple Templates

  9. Syncronization Template

  10. Dynamic Template

  11. NLE Chain Template

  12. Multi-Synch Template

  13. Enables Tracks Template

  14. Contingency Template

  15. Second Chance Template

  16. Circular Soft NLE Template

  17. Experimental Classes [Phase 2] • General Mix • Negative Interdependence • Very Dynamic • Circular Soft Interdependencies • Tight Deadlines • Uncertainty • Contingency • Big Interdependence • 100 Agent Mix 17

  18. General Mix • Basic template mix for most problems • 10–70 Agents • 400–3000 Methods • 1 fast fallback methods • 2 redundant methods • Template Mix • Simple[Sum] • Simple[SumAnd] • Dynamic • NLEChain - Multi Synch - Enables Tracks - Contingency - CircularSoftNLE[facilitates] 18

  19. General Mix, 10 Agents, Actual

  20. General Mix, 10 Agents, Detail Enables Tracks Template NLE Chain (start) Circular Soft NLE Template

  21. Negative Interdependence • Simple Template with lots of hinders and disables 21

  22. Very Dynamic • Dynamic Template with changes to deadlines, release times, and quality/duration distributions Random changes to: Release Times Deadlines Quality Distributions Duration Distributions 22

  23. Circular Soft Interdependencies • Circular facilitation creates hard optimization problem 23

  24. Tight Deadlines • Simple Templates with Sum and tight deadlines (must use alternative fallback or redundant methods) 24

  25. Uncertainty • Simple and Dynamic Templates; high uncertainty in duration/quality; 20% method failure Uncertainty make scheduling difficult

  26. Contingency

  27. Big Interdependence • NLE Template Mix: NLE Chains, Enables Tracks, Circular Soft NLEs plus random NLEs

  28. 100 Agent Mix (actual) • Similar to General Mix without Dynamic changes 28

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