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An Introduction to Artificial Intelligence CE 40417

An Introduction to Artificial Intelligence CE 40417. Chapter 12 – Planning and Acting in Real World Ramin Halavati (halavati@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. Outline.

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An Introduction to Artificial Intelligence CE 40417

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  1. An Introduction to Artificial IntelligenceCE 40417 Chapter 12 – Planning and Acting in Real World Ramin Halavati (halavati@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 • Resource:R(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 • What’s 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 • What’s Good: • Almost all real applications are HTN + some thing else. • It’s a heuristic to decrease the branching factor by a great level.

  19. NonDeterministic Domains • What if we don’t 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 don’t know all beforehand and we can’t 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 whenAtRightCleanRight • Left: when AtLeft CleanLeft whenAtRight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 don’t 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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