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An Introduction to Artificial Intelligence CE 40417

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Artificial Intelligence CE 40417 Chapter 12 Planning and Acting in Real World Ramin Halavati (halavati_at_ce.sharif.edu) In which we see how more expressive ... – PowerPoint PPT presentation

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Title: An Introduction to Artificial Intelligence CE 40417


1
An Introduction to Artificial Intelligence CE
40417
  • Chapter 12 Planning and Acting in Real World
  • Ramin Halavati (halavati_at_ce.sharif.edu)

In which we see how more expressive
representations and more interactive agent
architectures lead to planners that are useful in
real world.
2
Outline
  • Time, Schedules, and Resources
  • Hierarchical Task Network Planning
  • Planning and Acting in Nondeterministic Domains
  • Multi Agent Planning

3
Time, Schedules, Resources
  • Basic Planning
  • What to do and in which order?
  • Real World
  • What an When to do? Limited Resources.
  • JOB SHOP SCHEDULING

4
Job Shop Scheduling
5
Job Shop Scheduling
  • How to assign time to a partial order plan?

6
Critical Path Method (CPM)
  • Forward March
  • Set Earliest Start (ES)

7
Critical Path Method (CPM)
  • Backward March
  • Set Latest Start (LS)

8
Critical Path Method (CPM)
9
Limited Resources
  • Resources
  • Consumable vs. Reusable.
  • Notation
  • Aggregation
  • Immediate Effect
  • ResourceR(k)
  • Requirement / Temporary Effect

10
Limited Resources
  • No General Approach (NP-Hard)
  • Just Order the task so that the requirements are
    met.
  • Heuristic
  • Minimum Slack Algorithm
  • Give more priority to the task with least
    remaining slack.

11
Job Shop Scheduling, One Last Word.
  • Separated / Integrated Planning and Scheduling.
  • Semi Automatic

12
Hierarchical Planning
  • Hierarchical Task Network
  • At each level, only a small number of
    individual planning actions, then descend to
    lower levels to solve these for real.
  • At higher levels, the planner ignores internal
    effects of decompositions. But these have to be
    resolved at some level

13
HTN Sample
  • Construction Domain
  • Actions
  • Buy Land Money ? Land
  • Get Load Good Credit ? Money
  • Get Permit Land ? Permit
  • Hire Builder ? Contract
  • Construction Permit ? Contract ? House Built
  • Pay Builder Money ? House Built ? House

14
HTN Sample (cont)
  • Macro Action in Library
  • Build House

15
HTN Sample (cont)
16
HTN Sample (cont)
17
HTN Cons and Pros
  • Whats Bad?
  • Recursion?
  • Sub Task Sharing
  • Enjoy honey moon in Hawaii and raise a family.
  • Library
  • Enjoy Honey moon in Hawaaii Get Married , Go to
    Hawaii.
  • Raise Family Get Married, Have two children.

18
HTN Cons and Pros
  • Whats Good
  • Almost all real applications are HTN some thing
    else.
  • Its a heuristic to decrease the branching factor
    by a great level.

19
NonDeterministic Domains
  • What if we dont know all about situations and
    effects.
  • E.g.
  • Init A table and a chair of unknown colors.
  • Goal A table and a chair of the same colors.
  • Condition Painting may have flaws.

20
Sensorless Planning
  • We dont know all beforehand and we cant find it
    out, even when it is done.
  • Plan so that to reach the goal state, regardless
    of everything. (Coercion)
  • Not always possible.

21
Conditional Planning
  • We can check the state ahead, then perform the
    pre-planned program.
  • Sense Actions
  • Conditional Branches

22
Conditional Planning in Fully Observable Domains
  • Vacuum World
  • Left AtRight ? AtLeft ? ?AtRight
  • Left AtRight ?
  • (AtLeft ? ?AtRight)? (?AtLeft ? AtRight)
  • Suck when AtLeft?CleanLeft
  • when AtRight?CleanRight
  • Left when AtLeft? ?CleanLeft
  • when AtRight?AtLeft ?AtRight

23
Notation Expantion
  • Expanding Plan Notation
  • If (state) Then () else ()
  • If (AtLeft?CleanLeft ?CleanRight) Then
  • else Suck.

24
State Space
25
Conditional Planner
26
Unavoidable Loops in Conditional Planner
  • New Notation
  • Instead of just Left while (AtRight) Left

27
Partially Observable Domains
28
Partially Observable Domains
  • Easiest Approach
  • Assume set of current states and the next state
    sets are created, quite similar to
    non-deterministic actions case.

29
Execution Monitoring and Replanning
  • Check if the plan is going on is pre-decided? If
    not, replan based on current situation.

30
Execution Monitoring Replanning
  • Action Monitoring
  • See if current state is as it was supposed, if
    not, find a solution to return it to what it was
    (repair).

31
Execution Monitoring Replanning
  • Plan Monitoring
  • See if the previous plan is still wise?
  • Serendipity!
  • A precondition of future actions has failed and
    can not be recovered.

32
Execution Monitoring in Partially Observable
Domains
  • Things may fail and we dont know.
  • Sensing actions may be required
  • And they may need extra-planning.
  • We may stuck in futile attempts
  • The electronic key is incorrect, but we think it
    might be due to incorrect pushing in.

33
Continues Planner
  • Keep planning, sensing and executing
  • Which is not unlikely, such as maintenance
    planning, auto-pilot, plant control,

34
Continues Planner
35
Continues Planner
36
Continues Planner
37
Continuous Planner
  • POP
  • Missing Goal
  • A new goal has erupted. Just add it.
  • Open precondition
  • An action has lost its support links. Add a new
    causal link.
  • Causal Conflicts
  • A causal link is suddenly threatened. Choose an
    appropriate ordering.

38
Continuous Planner
  • POP
  • Unsupported Link
  • A link from start to something has suddenly last
    its true value. Remove it.
  • Redundant Action
  • An action no more produces something needed.
    Remove it.

39
Uncertainty is Over.
40
Multi Agent Planning
  • When there is more than one agent in the scene.
  • Competitive
  • Cooperative
  • Coordination
  • Communication

41
Cooperation
  • Multi Body Planning
  • One is in charge of all decisions
  • Having the agent as one of parameters
  • Go(R2D3, Right) Go(C3PO,Left).
  • Synchronization and Timing

42
Cooperation Multi Body
  • Joint Planning
  • Planning using action pairs
  • Exponentially Many Actions Actions Agents
  • Having Concurrent Actions List
  • Which actions happen together and which not, such
    as orders in POP.

43
Cooperation - Coordination
  • Accepting a prior Convention.
  • Everyone drive on his/her right side of the road.
  • Domain Independent
  • Choosing the first feasible action.
  • Producing all possible feasible actions and
    choosing the one which stands first in alphabetic
    order!

44
Cooperation Emergence
  • Evolutionary Emergent Behavior
  • Birds Flocking
  • Separation
  • Cohesion
  • Alignment
  • Ants.

45
Coop. - Communication
  • A short message expressing
  • the plan / next step.
  • A message expressing the next step.
  • Plan Recognition!

46
Competition
  • Minimax Conditional Planning

47
Essey Project Proposals
  • To Do.
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