1. 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.

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. 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)

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

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

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.