[PPT] - CS325 Artificial Intelligence Ch. 11, Advanced Planning Cengiz PowerPoint Presentation

SLIDE 1

CS325 Artificial Intelligence

Ch. 11, Advanced Planning

Cengiz Günay, Emory Univ. Spring 2013

Günay

Ch. 11, Advanced Planning

Spring 2013 1 / 12

SLIDE 2

Advanced Planning Concepts

This lecture: Time: Scheduling

Günay

Ch. 11, Advanced Planning

Spring 2013 2 / 12

SLIDE 3

Advanced Planning Concepts

This lecture: Time: Scheduling Resources: Consumables

Günay

Ch. 11, Advanced Planning

Spring 2013 2 / 12

SLIDE 4

Advanced Planning Concepts

This lecture: Time: Scheduling Resources: Consumables Active perception: Look and feel?

Günay

Ch. 11, Advanced Planning

Spring 2013 2 / 12

SLIDE 5

Advanced Planning Concepts

This lecture: Time: Scheduling Resources: Consumables Active perception: Look and feel? Hierarchical plans: Abstracting

Günay

Ch. 11, Advanced Planning

Spring 2013 2 / 12

SLIDE 6

Entry/Exit Surveys

Exit survey: Game Theory

Why don’t we take the mixed strategy if there is a dominant strategy? What advantage is gained by a player by looking irrational?

Entry survey: Advanced Planning (0.25 points of final grade)

Do you think a classical planning planning approach can be used for solving scheduling problems? What would be the advantage of making hierarchical plans?

Günay

Ch. 11, Advanced Planning

Spring 2013 3 / 12

SLIDE 7

I’m Late Again!

Fixed times for day start and class Durations

Wake up:10 minutes Eat: 30 minutes

Start (8am) Wake up Eat Class (10am)

Günay

Ch. 11, Advanced Planning

Spring 2013 4 / 12

SLIDE 8

I’m Late Again!

Fixed times for day start and class Durations

Wake up:10 minutes Eat: 30 minutes

Start (8am) Wake up Eat Class (10am) Earliest and latest start times of each event?

Günay

Ch. 11, Advanced Planning

Spring 2013 4 / 12

SLIDE 9

I’m Late Again!

Fixed times for day start and class Durations

Wake up:10 minutes Eat: 30 minutes

Start (8am) Wake up Eat Class (10am) Earliest and latest start times of each event?

Earliest(Wake up)=8am Latest(Wake up)=? Earliest(Eat)=? Latest(Eat)=10:00−00:30=9:30am

Günay

Ch. 11, Advanced Planning

Spring 2013 4 / 12

SLIDE 10

Multiple Paths to Finish: Car with 2 Engines

Critical path?

AddEngine1 AddWheels1 Inspect1 AddWheels2 Inspect2 AddEngine2 10 20 30 40 50 60 70 80 90 Günay

Ch. 11, Advanced Planning

Spring 2013 5 / 12

SLIDE 11

Multiple Paths to Finish: Car with 2 Engines

AddEngine1 AddWheels1 Inspect1 AddWheels2 Inspect2 AddEngine2 10 20 30 40 50 60 70 80 90 Start

[0,0]

AddEngine1

30 [ 0 , 1 5]

AddWheels1

30 [ 3 0, 4 5] 10

Inspect1

[60,75]

Finish

[85,85] 10

Inspect2

[75,75] 15

AddWheels2

[60,60] 60

AddEngine2

[0,0]

Günay

Ch. 11, Advanced Planning

Spring 2013 5 / 12

SLIDE 12

Multiple Paths to Finish: Car with 2 Engines

Start

[0,0]

AddEngine1

30 [ 0 , 1 5]

AddWheels1

30 [ 3 0, 4 5] 10

Inspect1

[60,75]

Finish

[85,85] 10

Inspect2

[75,75] 15

AddWheels2

[60,60] 60

AddEngine2

[0,0]

Earliest(Start)=0 Earliest(B)=maxA→B Earliest(A) + Duration(A) Latest(A)=maxA←B Latest(B) − Duration(A) Latest(Finish)=Earliest(Finish)

Günay

Ch. 11, Advanced Planning

Spring 2013 5 / 12

SLIDE 13

Resources: Can We Use Action Schemas?

Günay

Ch. 11, Advanced Planning

Spring 2013 6 / 12

SLIDE 14

Resources: Can We Use Action Schemas?

Will it reach the goal?

Günay

Ch. 11, Advanced Planning

Spring 2013 6 / 12

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Resources: Can We Use Action Schemas?

Will it reach the goal? No, one nut is missing.

Günay

Ch. 11, Advanced Planning

Spring 2013 6 / 12

SLIDE 16

Resources: Can We Use Action Schemas?

Will it reach the goal? No, one nut is missing. Depth first tree search: how many paths to eval? Small: 1, 4, 5 ? Medium: 4 + 5 or 4 × 5 ? Large: 4!, 5!, or 4! × 5! ?

Günay

Ch. 11, Advanced Planning

Spring 2013 6 / 12

SLIDE 17

Resources: Can We Use Action Schemas?

Will it reach the goal? No, one nut is missing. Depth first tree search: how many paths to eval? Small: 1, 4, 5 ? Medium: 4 + 5 or 4 × 5 ? Large: 4!, 5!, or 4! × 5! . Really inefficient!

Günay

Ch. 11, Advanced Planning

Spring 2013 6 / 12

SLIDE 18

Modify Action Schemas to Optimize Resource Problems

No need to try combinations of same resources. Define: Resources: Specify quantity. Use: Specify requirement. Consume: Removes resource.

Günay

Ch. 11, Advanced Planning

Spring 2013 7 / 12

SLIDE 19

Modify Action Schemas to Optimize Resource Problems

No need to try combinations of same resources. Define: Resources: Specify quantity. Use: Specify requirement. Consume: Removes resource.

Günay

Ch. 11, Advanced Planning

Spring 2013 7 / 12

SLIDE 20

Modify Action Schemas to Optimize Resource Problems

No need to try combinations of same resources. Define: Resources: Specify quantity. Use: Specify requirement. Consume: Removes resource. No exponential explotion anymore!

Günay

Ch. 11, Advanced Planning

Spring 2013 7 / 12

SLIDE 21

Hierarchical Planning: Abstraction

Remember Stanley: High-level goal:

Reach target at GPS coordinates Drive on road

Low-level actions:

Adjust steering wheel Press/release gas/break pedals

Günay

Ch. 11, Advanced Planning

Spring 2013 8 / 12

SLIDE 22

Hierarchical Planning: Abstraction

Remember Stanley: High-level goal:

Reach target at GPS coordinates Drive on road

Low-level actions:

Adjust steering wheel Press/release gas/break pedals

How to connect

1 high-level (abstract) planning with 2 low-level planning? Günay

Ch. 11, Advanced Planning

Spring 2013 8 / 12

SLIDE 23

Hierarchical Planning: Abstraction

Remember Stanley: High-level goal:

Reach target at GPS coordinates Drive on road

Low-level actions:

Adjust steering wheel Press/release gas/break pedals

How to connect

1 high-level (abstract) planning with 2 low-level planning?

Solution: Refinement

Günay

Ch. 11, Advanced Planning

Spring 2013 8 / 12

SLIDE 24

Refining Abstractions

Multiple ways to refine abstractions:

Günay

Ch. 11, Advanced Planning

Spring 2013 9 / 12

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Hierarchical Planning: Reachable States

Reachable?

Günay

Ch. 11, Advanced Planning

Spring 2013 10 / 12

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Hierarchical Planning: Reachable States

Reachable? No.

Günay

Ch. 11, Advanced Planning

Spring 2013 10 / 12

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Hierarchical Planning: Reachable States

Reachable? No. Found solution:

Günay

Ch. 11, Advanced Planning

Spring 2013 10 / 12

SLIDE 28

Hierarchical Planning: Reachable States

Reachable? No. Found solution: Now backtrack from solution.

Günay

Ch. 11, Advanced Planning

Spring 2013 10 / 12

SLIDE 29

Reachable States Question

Estimates of refinement: Underestimate: We reach it for sure. Overestimate: Possibly reachable. Below examples: Reachable? Yes, No, Maybe?

Günay

Ch. 11, Advanced Planning

Spring 2013 11 / 12

SLIDE 30

Reachable States Question

Estimates of refinement: Underestimate: We reach it for sure. Overestimate: Possibly reachable. Below examples: Reachable? Yes, No, Maybe? No Yes.

Günay

Ch. 11, Advanced Planning

Spring 2013 11 / 12

SLIDE 31

Reachable States Question

Estimates of refinement: Underestimate: We reach it for sure. Overestimate: Possibly reachable. Below examples: Reachable? Yes, No, Maybe? No Yes.

Günay

Ch. 11, Advanced Planning

Spring 2013 11 / 12

SLIDE 32

Reachable States Question

Estimates of refinement: Underestimate: We reach it for sure. Overestimate: Possibly reachable. Below examples: Reachable? Yes, No, Maybe? No Yes. Maybe.

Günay

Ch. 11, Advanced Planning

Spring 2013 11 / 12

SLIDE 33

Extending Planning with Observations

Sometimes the agent needs to look first.

Günay

Ch. 11, Advanced Planning

Spring 2013 12 / 12

SLIDE 34

Extending Planning with Observations

Sometimes the agent needs to look first.

Günay

Ch. 11, Advanced Planning

Spring 2013 12 / 12