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Capturing knowledge about the instances behaviour in probabilistic domains

Capturing knowledge about the instances behaviour in probabilistic domains. Sergio Jiménez Celorrio, Fernando Fernández, Daniel Borrajo Departamento de Informática Universidad Carlos III de Madrid. Outline. Motivation The System Experiments Conclusions. Motivation.

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Capturing knowledge about the instances behaviour in probabilistic domains

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  1. Capturing knowledge about the instances behaviour in probabilistic domains Sergio Jiménez Celorrio, Fernando Fernández, Daniel Borrajo Departamento de Informática Universidad Carlos III de Madrid

  2. Outline • Motivation • The System • Experiments • Conclusions

  3. Motivation • Planning in Probabilistic domains

  4. Motivation • Planning in Probabilistic domains • Without having a Probabilistic representation of the domain

  5. Motivation • Planning in Probabilistic domains • Without having a Probabilistic representation of the domain • Acquiring Probabilistic Information automatically • Repeating cycles of planning, execution and learning

  6. Motivation • Planning in Probabilistic domains • Without having a Probabilistic representation of the domain • Acquiring Probabilistic Information automatically • Repeating cycles of planning, execution and learning • Using the Probabilistic Information • Generating Control Knowledge

  7. The Planning Execution Learning Architecture Plan Executor Action (ai) Deterministic Domain Real World IPSS Planner (a1,a2,..,an) State (si) New problem Problem Execution Information (ai,si,s’i,gi) Control Knowledge Update Learning Robustness Table

  8. The Planning Execution Learning Architecture Plan Executor Action (ai) Deterministic Domain Real World IPSS Planner (a1,a2,..,an) State (si) New problem Problem Execution Information (ai,si,s’i,gi) Control Knowledge Update Learning Robustness Table

  9. The Planning Execution Learning Architecture Plan Executor Action (ai) Deterministic Domain Real World IPSS Planner (a1,a2,..,an) State (si) New problem Problem Execution Information (ai,si,s’i,gi) Control Knowledge Update Learning Robustness Table

  10. The Planning Execution Learning Architecture Plan Executor Action (ai) Deterministic Domain Real World IPSS Planner (a1,a2,..,an) State (si) New problem Problem Execution Information (ai,si,s’i,gi) Control Knowledge Update Learning Robustness Table

  11. The Planning Execution Learning Architecture Plan Executor Action (ai) Deterministic Domain Real World IPSS Planner (a1,a2,..,an) State (si) New problem Problem Execution Information (ai,si,s’i,gi) Control Knowledge Update Learning Robustness Table

  12. The Planning Execution Learning Architecture Plan Executor Action (ai) Deterministic Domain Real World IPSS Planner (a1,a2,..,an) State (si) New problem Problem Execution Information (ai,si,s’i,gi) Control Knowledge Update Learning Robustness Table

  13. The Planning Execution Learning Architecture Plan Executor Action (a1) Deterministic Domain Real World IPSS Planner (a1,a2,..,an) State (si) New problem Problem Execution Information (ai,si,s’i,gi) Control Knowledge Update Learning Robustness Table

  14. The Planning Execution Learning Architecture Plan Executor Action (ai) Deterministic Domain Real World IPSS Planner (a1,a2,..,an) State (s1) New problem Problem Execution Information (ai,si,s’i,gi) Control Knowledge Update Learning Robustness Table

  15. The Planning Execution Learning Architecture Plan Executor Action (ai) Deterministic Domain Real World IPSS Planner (a1,a2,..,an) State (si) New problem Problem Execution Information (ai,si,s’i,gi) Control Knowledge Update Learning Robustness Table

  16. The Planning Execution Learning Architecture State1 Pick-up C B A

  17. The Planning Execution Learning Architecture State1 State2 Pick-up C success C B B A A

  18. The Planning Execution Learning Architecture State1 Pick-up C B A

  19. The Planning Execution Learning Architecture State1 State2 Pick-up failure C B B C A A

  20. The Planning Execution Learning Architecture Plan Executor Action (ai) Deterministic Domain Real World IPSS Planner (a1,a2,..,an) State (si) New problem Problem Execution Information (ai,si,s’i,gi) Control Knowledge Update Learning Robustness Table

  21. The Planning Execution Learning Architecture Plan Executor Action (ai) Deterministic Domain Real World IPSS Planner (a1,a2,..,an) State (si) New problem Problem Execution Information (ai,si,s’i,gi) Control Knowledge Update Learning Robustness Table

  22. The Robustness Table

  23. Updating The Robustness Table pick-up-block-from (block0 block1) - Success

  24. Updating The Robustness Table pick-up-block-from (block0 block1) - Success

  25. Updating The Robustness Table pick-up-block-from (block0 block1) - Success

  26. Updating The Robustness Table pick-up-block-from (block0 block1) - Failure

  27. Updating The Robustness Table pick-up-block-from (block0 block1) - Failure

  28. Updating The Robustness Table pick-up-block-from (block0 block1) - Failure

  29. The Planning Execution Learning Architecture Plan Executor Action (ai) Deterministic Domain Real World IPSS Planner (a1,a2,..,an) State (si) New problem Problem Execution Information (ai,si,s’i,gi) Control Knowledge Update Learning Robustness Table

  30. The Planning Execution Learning Architecture Plan Executor Action (ai) Deterministic Domain Real World IPSS Planner (a1,a2,..,an) State (si) New problem Problem Execution Information (ai,si,s’i,gi) Control Knowledge Update Learning Robustness Table

  31. The Control Knowledge (control-rule prefer-operator-for-holding (if (and (current-goal (holding <robot> <block>)) (candidate-operator <op1>) (candidate-operator <op2>) (robustness-op-more-than <op1> <op2>))) (then prefer operator <op1> <op2>))

  32. The Control Knowledge (control-rule prefer-bindings-for-put-down-block-on-for-on-top-of (if (and (current-goal (on-top-of <block-1> <object-1>)) (current-operator put-down-block-on) (candidate-bindings ((<robot> . <robot-1>) (<top> . <block-1>) (<bottom> . <object-1>))) (candidate-bindings ((<robot> . <robot-2>) (<top> . <block-1>) (<bottom> . <object-1>))) (robustness-bindings-more-than put-down-block-on1 ((<robot> . <robot-1>) (<top> . <block-1>) (<bottom> . <object-1>)) ((<robot> . <robot-2>) (<top> . <block-1>) (<bottom> . <object-1>))))) (then prefer bindings ((<robot> . <robot-1>) (<top> . <block-1>) (<bottom> . <object-1>)) ((<robot> . <robot-2>) (<top> . <block-1>) (<bottom> . <object-1>))))

  33. The Experiments Probabilistic Domain Action (ai) IPC4-Simulator State (si)

  34. The Experiments Probabilistic Domain Action (ai) IPC4-Simulator State (si)

  35. The Experiments Probabilistic Domain Action (ai) IPC4-Simulator State (si)

  36. The Experiments Probabilistic Domain Action (ai) IPC4-Simulator State (si)

  37. The Experiments Probabilistic Domain Plan Executor Action (ai) Deterministic Domain IPC4-Simulator IPSS Planner (a1,a2,..,an) State (si) New problem Problem Execution Information (ai,si,s’i,gi) Update Learning Robustness Table

  38. The Experiments Probabilistic Domain Plan Executor Action (ai) Deterministic Domain IPC4-Simulator IPSS Planner (a1,a2,..,an) State (si) New problem Problem Execution Information (ai,si,s’i,gi) Update Learning Robustness Table

  39. The Experiments C A B

  40. The Experiments Probabilistic Domain Plan Executor Action (ai) Deterministic Domain IPC4-Simulator IPSS Planner (a1,a2,..,an) State (si) New problem Problem Execution Information (ai,si,s’i,gi) Update Learning Robustness Table

  41. The Experiments Probabilistic Domain Plan Executor Action (ai) Deterministic Domain IPC4-Simulator IPSS Planner (a1,a2,..,an) State (si) New problem Problem Execution Information (ai,si,s’i,gi) Update Learning Robustness Table

  42. The Experiments

  43. The Experiments • 5 blocks • 8 blocks • 11 blocks

  44. The Experiments • 5 blocks • 8 blocks • 11 blocks

  45. Conclusions • Current Work • Capturing instances probabilistic behaviour • Generating Control Knowledge

  46. Conclusions • Current Work • Capturing instances probabilistic behaviour • Generating Control Knowledge • Future Work • Capturing State dependant behaviour • Generating State dependant Control Knowledge

  47. The Robustness Table

  48. The Robustness Table

  49. State Dependant Control Knowledge (control-rule prefer-operator-for-holding (if (and (current-goal (holding <robot> <block>)) (true-in-state (<state>)) (candidate-operator <op1>) (candidate-operator <op2>) (robustness-op-more-than <state><op1> <op2>))) (then prefer operator <op1> <op2>))

  50. Conclusions • Current Work • Capturing instances probabilistic behaviour • Generating Control Knowledge • Future Work • Capturing State dependant behaviour • Generating State dependant Control Knowledge • Escalation Problem • Robustness Table Size

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