1. Utilizing Problem Structure in Local Search: The Planning Benchmarks as a Case Study Jőrg Hoffmann Alberts-Ludwigs-University Freiburg

2. Overview The Planning Benchmarks A Local Search Approach Local Search Topology Conclusion

3. Overview The Planning Benchmarks A Local Search Approach –FF Algorithms –AIPS´00 Competition Local Search Topology Conclusion

4. Overview The Planning Benchmarks A Local Search Approach Local Search Topology –Gathering Insights: Looking at Small Instances –The Topology of h+ –The Topology of Approximating h+ Conclusion

5. Overview The Planning Benchmarks A Local Search Approach Local Search Topology Conclusion

6. „The“ Planning Benchmarks Assembly, Blocksworld-arm, Blocksworld- no-arm, Briefcaseworld,Ferry, Fridge, Freecell, Grid, Gripper, Hanoi, Logistics, Miconic-ADL, Miconic-SIMPLE, Miconic- STRIPS, Movie, Mprime, Mystery, Schedule, Simple-Tsp, Tyreworld

7. „The“ Planning Benchmarks Assembly, Blocksworld-arm, Blocksworld- no-arm, Briefcaseworld,Ferry, Fridge, Freecell, Grid, Gripper, Hanoi, Logistics, Miconic-ADL, Miconic-SIMPLE, Miconic- STRIPS, Movie, Mprime, Mystery, Schedule, Simple-Tsp, Tyreworld

8. „The“ Planning Benchmarks Assembly, Blocksworld-arm, Blocksworld- no-arm, Briefcaseworld,Ferry, Fridge, Freecell, Grid, Gripper, Hanoi, Logistics, Miconic-ADL, Miconic-SIMPLE, Miconic- STRIPS, Movie, Mprime, Mystery, Schedule, Simple-Tsp, Tyreworld

9. „The“ Planning Benchmarks Assembly, Blocksworld-arm, Blocksworld- no-arm, Briefcaseworld,Ferry, Fridge, Freecell, Grid, Gripper, Hanoi, Logistics, Miconic-ADL, Miconic-SIMPLE, Miconic- STRIPS, Movie, Mprime, Mystery, Schedule, Simple-Tsp, Tyreworld

10. Overview The Planning Benchmarks A Local Search Approach –FF Algorithms –AIPS´00 Competition Local Search Topology Conclusion

11. FF Algorithms FF can be seen as a refinement of HSP 1.0: –search forward in the state space –relax planning task by ignoring delete lists Main Differences [Hoffmann & Nebel 2001] –heuristic (different approximation of h+) –search strategy (different hill-climbing variant) –pruning technique (new)

12. FF Algorithms - Heuristic Approach often used in heuristic search: relax problem, solve relaxation In planning: ignore delete lists [Bonet et al.1997] Optimal relaxed solution length h+ admissible but NP-hard to compute [Bylander 1994] HSP 1.0: approximate h+ by weight value sums FF: approximate h+ by running a relaxed version of GRAPHPLAN [Blum & Furst 1997]

13. FF Algorithms - Search Local search as state evaluation is costly HSP 1.0: (standard) hill-climbing FF: enforced hill-climbing –start in initial state –in a state S, do breadth first search for S´ such that h(S´) < h(S) Intuition: hill-climbing needs more „motion force“ towards the goal

14. FF Algorithms - Pruning Observation: often, GRAPHPLAN´s relaxed solutions are close to what needs to be done, at least in first step –in Gripper, for example, actions that drop balls into room A are never selected Restrict action choice in any state S to those selected by the first step of the relaxed plan for S

15. Overview The Planning Benchmarks A Local Search Approach –FF Algorithms –AIPS´00 Competition Local Search Topology Conclusion

16. AIPS´00 Competition Planning systems competition alongside AIPS´00 [Bacchus 2001] 15 participants, 12 in fully automated track 5 domains, around 50 - 200 scaling instances each we briefly look at the runtime curves in the fully automated track

17. AIPS´00 - Logistics

18. AIPS´00 - Blocksworld(-arm)

19. AIPS´00 - Schedule

20. AIPS´00 - Freecell

21. AIPS´00 - Miconic-ADL

22. AIPS´00 Competition As a result of the competition, FF –was nominated „Group A Distinguished Performance Planning System“ (together with TalPlanner from the hand-tailored track) –won the Schindler Award for Best Performance in the Miconic domain, ADL track Note: we have only briefly seen one part of the competition

23. FF vs. IPP in Gripper

24. Overview The Planning Benchmarks A Local Search Approach Local Search Topology –Gathering Insights: Looking at Small Instances –The Topology of h+ –The Topology of Approximating h+ Conclusion

25. Local Search Topology The behaviour of local search depends crucially on the topology of the search space (studied in SAT, e.g. [Frank et al. 1997]) Identify, following [Frank et al. 1997], the topology of the benchmarks, under h+ and FF´s approximative h+

26. Overview The Planning Benchmarks A Local Search Approach Local Search Topology –Gathering Insights: Looking at Small Instances –The Topology of h+ –The Topology of Approximating h+ Conclusion

27. Gathering Insights Start by looking at small instances: [Hoffmann 2001] –in the 20 domains, randomly generate suits of small examples –build the state spaces and compute h+ to all states (resp. FF‘s approximation of h+) –measure parameters of the resulting local search topology (definitions adapted from [Frank et al.1997])

28. Topological Phenomena Dead ends Measured: how many are there? Recognized? (i.e. h+ = ∞)?

29. Topological Phenomena Local Minima Measured (amongst other things): how many are there?

30. Topological Phenomena Benches Measured (amongst other things): maximal exit distance

31. h+ Topology in Small Instances In lowermost class, enforced hill-climbing is polynomial! FF approximation similar: some, but few local minima

32. A Visualized Example: Gripper

33. A Visualized Example: Hanoi

35. Overview The Planning Benchmarks A Local Search Approach Local Search Topology –Gathering Insights: Looking at Small Instances –The Topology of h+ –The Topology of Approximating h+ Conclusion

36. The proven Topology of h+

37. Reasons for h+ Topology Invertible actions: actions a to which there exists an inverse action undoing exactly a‘s effects Example Logistics –load obj truck --- unload obj truck –drive loc1 loc2 --- drive loc2 loc1 Implies non-existence of dead ends, and of local minima with: see next slide

38. Reasons for h+ Topology Actions that are respected by the relaxation: if a starts an optimal plan from S, then a also starts an optimal relaxed plan from S Example Logistics –load obj truck: obj must be transported, and there is no other way of doing that –drive loc1 loc2: some obj must be loaded/unloaded at loc2, again no other choice for the relaxed plan If all actions are invertible and respected by the relaxation, then there are no local minima under h+

39. Overview The Planning Benchmarks A Local Search Approach Local Search Topology –Gathering Insights: Looking at Small Instances –The Topology of h+ –The Topology of Approximating h+ Conclusion

40. The Topology of Approximating h+ Dead ends behave provably the same In domains where no local minima exist under h+: –check local minima percentage under approximative (FF) heuristic in large instances In domains where maximal exit distance constant under h+: –check maximum over exit distances in large instances

41. Investigating Large Instances Take Samples from State Spaces: (following [Frank et al. 1997]) –randomly generate suits of large instances –repeatedly, walk a random number of random steps into the state space, ending in a state S –check whether S lies on a local minimum, and what the exit distance is –visualize data against generator parameters

42. Logistics: Local Minima

43. Logistics: Maximal Exit Distance

44. Overview The Planning Benchmarks A Local Search Approach Local Search Topology Conclusion

45. Conclusion - Planning Critically: time to move on to other benchmarks? –agree: time and resources –disagree: only NP-hard problems for benchmarking Positively: we have a good suboptimal planner! –we know where it works well –we know why it works well

46. Conclusion - Local Search It is certainly an extreme example, but nevertheless: Utilizing problem structure can be crucial Thanks to: Bernhard Nebel; Jana Koehler; for doing successful local search (though you‘d normally first identify the structure, then try to utilize it)