angelic hierarchical planning
play

Angelic Hierarchical Planning: Optimal and Online Algorithms - PowerPoint PPT Presentation

Feb 08, 2023 •261 likes •1.39k views

Angelic Hierarchical Planning: Optimal and Online Algorithms Bhaskara Marthi Stuart Russell Jason Wolfe MIT/Willow Garage UC Berkeley UC Berkeley bhaskara@csail.mit.edu russell@cs.berkeley.edu jawolfe@cs.berkeley.edu ICAPS 08 1

  1. Deterministic Planning Problems • Here, a planning problem = • State space S • Initial state s 0 , terminal set G • Primitive action set • Transition function: S × A → S • Cost function : S × A → R ∪ { ∞ } S s 0 -5 4 ∞ 8 G ∞ 3 Transitions & costs for action a 1 7

  2. Running Example: Warehouse World Domain • Elaborated Blocks World with discrete spatial constraints • Gripper must stay in bounds C • Can’t pass through blocks • Can only turn at top row B A T1 T2 T3 T4 8

  3. Running Example: Warehouse World Domain • Elaborated Blocks World with discrete spatial constraints • Gripper must stay in bounds C • Can’t pass through blocks • Can only turn at top row B A • All actions have cost 1 T1 T2 T3 T4 8

  4. Running Example: Warehouse World Domain • Elaborated Blocks World with discrete spatial constraints • Gripper must stay in bounds C • Can’t pass through blocks • Can only turn at top row B A • All actions have cost 1 • Goal: have C on T4 T1 T2 T3 T4 • Can’t just move directly L, D, GetR, U, Turn, D, PutL, • Final plan has 22 steps R, R, D, GetL, L, PutL, U, L, GetL, U, Turn, R, D, D, PutR 8

  5. Running Example: Warehouse World Domain • Elaborated Blocks World with discrete spatial constraints • Gripper must stay in bounds C • Can’t pass through blocks • Can only turn at top row B A • All actions have cost 1 • Goal: have C on T4 T1 T2 T3 T4 • Can’t just move directly L, D, GetR, U, Turn, D, PutL, • Final plan has 22 steps R, R, D, GetL, L, PutL, U, L, GetL, U, Turn, R, D, D, PutR 8

  6. Running Example: Warehouse World Domain • Elaborated Blocks World with discrete spatial constraints • Gripper must stay in bounds C • Can’t pass through blocks • Can only turn at top row B A • All actions have cost 1 • Goal: have C on T4 T1 T2 T3 T4 • Can’t just move directly L, D, GetR, U, Turn, D, PutL, • Final plan has 22 steps R, R, D, GetL, L, PutL, U, L, GetL, U, Turn, R, D, D, PutR 8

  7. Running Example: Warehouse World Domain • Elaborated Blocks World with discrete spatial constraints • Gripper must stay in bounds C • Can’t pass through blocks • Can only turn at top row B A • All actions have cost 1 • Goal: have C on T4 T1 T2 T3 T4 • Can’t just move directly L, D, GetR, U, Turn, D, PutL, • Final plan has 22 steps R, R, D, GetL, L, PutL, U, L, GetL, U, Turn, R, D, D, PutR 8

  8. Running Example: Warehouse World Domain • Elaborated Blocks World with C discrete spatial constraints • Gripper must stay in bounds • Can’t pass through blocks • Can only turn at top row B A • All actions have cost 1 • Goal: have C on T4 T1 T2 T3 T4 • Can’t just move directly L, D, GetR, U, Turn, D, PutL, • Final plan has 22 steps R, R, D, GetL, L, PutL, U, L, GetL, U, Turn, R, D, D, PutR 8

  9. Running Example: Warehouse World Domain • Elaborated Blocks World with C discrete spatial constraints • Gripper must stay in bounds • Can’t pass through blocks • Can only turn at top row B A • All actions have cost 1 • Goal: have C on T4 T1 T2 T3 T4 • Can’t just move directly L, D, GetR, U, Turn, D, PutL, • Final plan has 22 steps R, R, D, GetL, L, PutL, U, L, GetL, U, Turn, R, D, D, PutR 8

  10. Running Example: Warehouse World Domain • Elaborated Blocks World with discrete spatial constraints • Gripper must stay in bounds C • Can’t pass through blocks • Can only turn at top row B A • All actions have cost 1 • Goal: have C on T4 T1 T2 T3 T4 • Can’t just move directly L, D, GetR, U, Turn, D, PutL, • Final plan has 22 steps R, R, D, GetL, L, PutL, U, L, GetL, U, Turn, R, D, D, PutR 8

  11. Running Example: Warehouse World Domain • Elaborated Blocks World with discrete spatial constraints • Gripper must stay in bounds C • Can’t pass through blocks • Can only turn at top row B A • All actions have cost 1 • Goal: have C on T4 T1 T2 T3 T4 • Can’t just move directly L, D, GetR, U, Turn, D, PutL, • Final plan has 22 steps R, R, D, GetL, L, PutL, U, L, GetL, U, Turn, R, D, D, PutR 8

  12. Running Example: Warehouse World Domain • Elaborated Blocks World with discrete spatial constraints • Gripper must stay in bounds C • Can’t pass through blocks • Can only turn at top row B A • All actions have cost 1 • Goal: have C on T4 T1 T2 T3 T4 • Can’t just move directly L, D, GetR, U, Turn, D, PutL, • Final plan has 22 steps R, R, D, GetL, L, PutL, U, L, GetL, U, Turn, R, D, D, PutR 8

  13. Running Example: Warehouse World Domain • Elaborated Blocks World with discrete spatial constraints • Gripper must stay in bounds C • Can’t pass through blocks • Can only turn at top row B A • All actions have cost 1 • Goal: have C on T4 T1 T2 T3 T4 • Can’t just move directly L, D, GetR, U, Turn, D, PutL, • Final plan has 22 steps R, R, D, GetL, L, PutL, U, L, GetL, U, Turn, R, D, D, PutR 8

  14. Running Example: Warehouse World Domain • Elaborated Blocks World with discrete spatial constraints • Gripper must stay in bounds C • Can’t pass through blocks • Can only turn at top row B A • All actions have cost 1 • Goal: have C on T4 T1 T2 T3 T4 • Can’t just move directly L, D, GetR, U, Turn, D, PutL, • Final plan has 22 steps R, R, D, GetL, L, PutL, U, L, GetL, U, Turn, R, D, D, PutR 8

  15. Running Example: Warehouse World Domain • Elaborated Blocks World with discrete spatial constraints • Gripper must stay in bounds C • Can’t pass through blocks • Can only turn at top row B A • All actions have cost 1 • Goal: have C on T4 T1 T2 T3 T4 • Can’t just move directly L, D, GetR, U, Turn, D, PutL, • Final plan has 22 steps R, R, D, GetL, L, PutL, U, L, GetL, U, Turn, R, D, D, PutR 8

  16. Running Example: Warehouse World Domain • Elaborated Blocks World with discrete spatial constraints • Gripper must stay in bounds C • Can’t pass through blocks • Can only turn at top row B A • All actions have cost 1 • Goal: have C on T4 T1 T2 T3 T4 • Can’t just move directly L, D, GetR, U, Turn, D, PutL, • Final plan has 22 steps R, R, D, GetL, L, PutL, U, L, GetL, U, Turn, R, D, D, PutR 8

  17. Running Example: Warehouse World Domain • Elaborated Blocks World with discrete spatial constraints • Gripper must stay in bounds C • Can’t pass through blocks • Can only turn at top row A B • All actions have cost 1 • Goal: have C on T4 T1 T2 T3 T4 • Can’t just move directly L, D, GetR, U, Turn, D, PutL, • Final plan has 22 steps R, R, D, GetL, L, PutL, U, L, GetL, U, Turn, R, D, D, PutR 8

  18. Running Example: Warehouse World Domain • Elaborated Blocks World with discrete spatial constraints • Gripper must stay in bounds C • Can’t pass through blocks • Can only turn at top row A B • All actions have cost 1 • Goal: have C on T4 T1 T2 T3 T4 • Can’t just move directly L, D, GetR, U, Turn, D, PutL, • Final plan has 22 steps R, R, D, GetL, L, PutL, U, L, GetL, U, Turn, R, D, D, PutR 8

  19. Running Example: Warehouse World Domain • Elaborated Blocks World with discrete spatial constraints • Gripper must stay in bounds C • Can’t pass through blocks • Can only turn at top row A B • All actions have cost 1 • Goal: have C on T4 T1 T2 T3 T4 • Can’t just move directly L, D, GetR, U, Turn, D, PutL, • Final plan has 22 steps R, R, D, GetL, L, PutL, U, L, GetL, U, Turn, R, D, D, PutR 8

  20. Running Example: Warehouse World Domain • Elaborated Blocks World with discrete spatial constraints • Gripper must stay in bounds C • Can’t pass through blocks • Can only turn at top row A B • All actions have cost 1 • Goal: have C on T4 T1 T2 T3 T4 • Can’t just move directly L, D, GetR, U, Turn, D, PutL, • Final plan has 22 steps R, R, D, GetL, L, PutL, U, L, GetL, U, Turn, R, D, D, PutR 8

  21. Running Example: Warehouse World Domain • Elaborated Blocks World with C discrete spatial constraints • Gripper must stay in bounds • Can’t pass through blocks • Can only turn at top row A B • All actions have cost 1 • Goal: have C on T4 T1 T2 T3 T4 • Can’t just move directly L, D, GetR, U, Turn, D, PutL, • Final plan has 22 steps R, R, D, GetL, L, PutL, U, L, GetL, U, Turn, R, D, D, PutR 8

  22. Running Example: Warehouse World Domain • Elaborated Blocks World with C discrete spatial constraints • Gripper must stay in bounds • Can’t pass through blocks • Can only turn at top row A B • All actions have cost 1 • Goal: have C on T4 T1 T2 T3 T4 • Can’t just move directly L, D, GetR, U, Turn, D, PutL, • Final plan has 22 steps R, R, D, GetL, L, PutL, U, L, GetL, U, Turn, R, D, D, PutR 8

  23. Running Example: Warehouse World Domain • Elaborated Blocks World with C discrete spatial constraints • Gripper must stay in bounds • Can’t pass through blocks • Can only turn at top row A B • All actions have cost 1 • Goal: have C on T4 T1 T2 T3 T4 • Can’t just move directly L, D, GetR, U, Turn, D, PutL, • Final plan has 22 steps R, R, D, GetL, L, PutL, U, L, GetL, U, Turn, R, D, D, PutR 8

  24. Running Example: Warehouse World Domain • Elaborated Blocks World with discrete spatial constraints • Gripper must stay in bounds C • Can’t pass through blocks • Can only turn at top row A B • All actions have cost 1 • Goal: have C on T4 T1 T2 T3 T4 • Can’t just move directly L, D, GetR, U, Turn, D, PutL, • Final plan has 22 steps R, R, D, GetL, L, PutL, U, L, GetL, U, Turn, R, D, D, PutR 8

  25. Running Example: Warehouse World Domain • Elaborated Blocks World with discrete spatial constraints • Gripper must stay in bounds • Can’t pass through blocks • Can only turn at top row C A B • All actions have cost 1 • Goal: have C on T4 T1 T2 T3 T4 • Can’t just move directly L, D, GetR, U, Turn, D, PutL, • Final plan has 22 steps R, R, D, GetL, L, PutL, U, L, GetL, U, Turn, R, D, D, PutR 8

  26. Running Example: Warehouse World Domain • Elaborated Blocks World with discrete spatial constraints • Gripper must stay in bounds • Can’t pass through blocks • Can only turn at top row A B C • All actions have cost 1 • Goal: have C on T4 T1 T2 T3 T4 • Can’t just move directly L, D, GetR, U, Turn, D, PutL, • Final plan has 22 steps R, R, D, GetL, L, PutL, U, L, GetL, U, Turn, R, D, D, PutR 8

  27. Running Example: Warehouse World HLAs L D GetR U Turn D PutL 9

  28. Running Example: Warehouse World HLAs Nav( 2,3 ) Nav( 3,3 ) Nav( 2,3 ) L D GetR U Turn D PutL 9

  29. Running Example: Warehouse World HLAs NavT( 2,3 ) NavT( 2,3 ) Nav( 2,3 ) Nav( 3,3 ) Nav( 2,3 ) L D GetR U Turn D PutL 9

  30. Running Example: Warehouse World HLAs Move( C,A ) NavT( 2,3 ) NavT( 2,3 ) Nav( 2,3 ) Nav( 3,3 ) Nav( 2,3 ) L D GetR U Turn D PutL 9

  31. Running Example: Warehouse World HLAs Act ... Move( C,A ) ... NavT( 2,3 ) NavT( 2,3 ) ... Nav( 2,3 ) Nav( 3,3 ) Nav( 2,3 ) ... L D GetR U Turn D PutL 9

  32. Running Example: Warehouse World HLAs • Plans of interest are primitive refinements of special HLA Act [Act] 10

  33. Running Example: Warehouse World HLAs • Plans of interest are primitive refinements of special HLA Act • Each HLA has a set of immediate [Act] refinements into action sequences ... iff at G [Move( B,C ), Act] [ ] 10

  34. Running Example: Warehouse World HLAs • Plans of interest are primitive refinements of special HLA Act • Each HLA has a set of immediate [Act] refinements into action sequences ... iff at G [Move( B,C ), Act] [ ] ... [NavT( left of B ), GetR, NavT( left of target ), PutR] ... 10

  35. Running Example: Warehouse World HLAs • Plans of interest are primitive refinements of special HLA Act • Each HLA has a set of immediate [Act] refinements into action sequences ... iff at G [Move( B,C ), Act] [ ] ... [NavT( left of B ), GetR, NavT( left of target ), PutR] ... ... ... 10

  36. Abstract Lookahead Trees (ALTs) • ALTs generalize lookahead trees for flat algs (e.g., A*) 11

  37. Abstract Lookahead Trees (ALTs) • ALTs generalize lookahead trees for flat algs (e.g., A*) • Represent a set of potential plans Act 11

  38. Abstract Lookahead Trees (ALTs) • ALTs generalize lookahead trees for flat algs (e.g., A*) • Represent a set of potential plans • Basic operation: refine a plan (replace with all refs. at some HLA) Act 11

  39. Abstract Lookahead Trees (ALTs) • ALTs generalize lookahead trees for flat algs (e.g., A*) • Represent a set of potential plans • Basic operation: refine a plan (replace with all refs. at some HLA) Act 11

  40. Abstract Lookahead Trees (ALTs) • ALTs generalize lookahead trees for flat algs (e.g., A*) • Represent a set of potential plans • Basic operation: refine a plan (replace with all refs. at some HLA) Move( C,A ) Act Act 11

  41. Abstract Lookahead Trees (ALTs) • ALTs generalize lookahead trees for flat algs (e.g., A*) • Represent a set of potential plans • Basic operation: refine a plan (replace with all refs. at some HLA) Move( C,A ) Act Act Move( A,C ) Act 11

  42. Abstract Lookahead Trees (ALTs) • ALTs generalize lookahead trees for flat algs (e.g., A*) • Represent a set of potential plans • Basic operation: refine a plan (replace with all refs. at some HLA) Move( C,A ) Act Act Move( A,C ) Act 11

  43. Abstract Lookahead Trees (ALTs) • ALTs generalize lookahead trees for flat algs (e.g., A*) • Represent a set of potential plans • Basic operation: refine a plan (replace with all refs. at some HLA) Move( C,A ) Act Act Move( A,C ) Act 11

  44. Abstract Lookahead Trees (ALTs) • ALTs generalize lookahead trees for flat algs (e.g., A*) • Represent a set of potential plans • Basic operation: refine a plan (replace with all refs. at some HLA) Act Move( B,C ) Move( C,A ) Act Act Move( A,C ) Act 11

  45. Abstract Lookahead Trees (ALTs) • ALTs generalize lookahead trees for flat algs (e.g., A*) • Represent a set of potential plans • Basic operation: refine a plan (replace with all refs. at some HLA) Act Move( B,C ) Move( C,A ) Act Move( C,B ) Act Act Move( A,C ) Act 11

  46. Abstract Lookahead Trees (ALTs) • ALTs generalize lookahead trees for flat algs (e.g., A*) • Represent a set of potential plans • Basic operation: refine a plan (replace with all refs. at some HLA) Act Move( B,C ) Move( C,A ) Act Move( C,B ) Act Act Move( A,C ) Act 11

  47. Abstract Lookahead Trees (ALTs) • ALTs generalize lookahead trees for flat algs (e.g., A*) • Represent a set of potential plans • Basic operation: refine a plan (replace with all refs. at some HLA) Act Move( B,C ) Move( C,A ) Act Move( C,B ) Act Act Move( A,C ) Act 11

  48. Abstract Lookahead Trees (ALTs) • ALTs generalize lookahead trees for flat algs (e.g., A*) • Represent a set of potential plans • Basic operation: refine a plan (replace with all refs. at some HLA) Act ) C , B ( e v o M . . . Act Move( B,C ) Nav( x C -1,y C ) Move( C,A ) Act Move( C,B ) Act Act Move( A,C ) Act 11

  49. Abstract Lookahead Trees (ALTs) • ALTs generalize lookahead trees for flat algs (e.g., A*) • Represent a set of potential plans • Basic operation: refine a plan (replace with all refs. at some HLA) • Nodes have optimistic & pessimistic valuations 6/6 8/8 3/3 Act ) C , B ( e v o M 1/1 . . . 5/7 7/9 Act Move( B,C ) Nav( x C -1,y C ) 2/4 Move( C,A ) Act 4/8 9/ ∞ 0/0 Move( C,B ) Act Act 3/ ∞ 8/ ∞ Move( A,C ) Act 11

  50. Modeling HLAs • An HLA is fully characterized by planning problem + hierarchy NavT( 0,1 ) 12

  51. Modeling HLAs • An HLA is fully characterized by planning problem + hierarchy But without abstraction, lose benefits of hierarchy • NavT( 0,1 ) 6 8 10 ... ... 7 5 12

  52. Modeling HLAs • An HLA is fully characterized by planning problem + hierarchy But without abstraction, lose benefits of hierarchy • • Extension of idea from “Angelic Semantics for HLAs” [MRW ‘07]: Valuation of HLA h from state s: • For each s’, min cost of any primitive refinement of h that takes s to s’ • NavT( 0,1 ) 6 � � 6 � � � 5 5 � 12

  53. Modeling HLAs • An HLA is fully characterized by planning problem + hierarchy But without abstraction, lose benefits of hierarchy • • Extension of idea from “Angelic Semantics for HLAs” [MRW ‘07]: Valuation of HLA h from state s: • For each s’, min cost of any primitive refinement of h that takes s to s’ • Exact description of h = valuation of h from each s • NavT( 0,1 ) � � � � � � 6 � 6 � 6 � � � � � ... � � 5 � 5 � 5 � 12

  54. Modeling HLAs • An HLA is fully characterized by planning problem + hierarchy But without abstraction, lose benefits of hierarchy • • Extension of idea from “Angelic Semantics for HLAs” [MRW ‘07]: Valuation of HLA h from state s: • For each s’, min cost of any primitive refinement of h that takes s to s’ • Exact description of h = valuation of h from each s • But this description has no compact, efficient representation in general • NavT( 0,1 ) � � � � � � 6 � 6 � 6 � � � � � ... � � 5 � 5 � 5 � 12

  55. Optimistic and Pessimistic Valuations • Instead, use approximate valuations � � Exact 6 4 3 1 5 � 13

  56. Optimistic and Pessimistic Valuations • Instead, use approximate valuations • We choose a simple form: reachable set + cost bound on set � � Exact 6 4 3 1 5 � 13

  57. Optimistic and Pessimistic Valuations • Instead, use approximate valuations • We choose a simple form: reachable set + cost bound on set • Optimistic valuations never overestimate best achievable cost 1 Optimistic � � Exact 6 4 3 1 5 � 13

  58. Optimistic and Pessimistic Valuations • Instead, use approximate valuations • We choose a simple form: reachable set + cost bound on set • Optimistic valuations never overestimate best achievable cost • Pessimistic valuations never underestimate best achievable cost 1 / 4 Optimistic � � Exact 6 4 Pessimistic 3 1 5 � 13

  59. Representing Descriptions: NCSTRIPS 14

  60. Representing Descriptions: NCSTRIPS • Descriptions specify propositions (possibly) added/deleted by HLA 14

  61. Representing Descriptions: NCSTRIPS • Descriptions specify propositions (possibly) added/deleted by HLA NavT( x t ,y t ) (Pre: At( x s ,y s )) 14

  62. Representing Descriptions: NCSTRIPS • Descriptions specify propositions (possibly) added/deleted by HLA • Also include a cost bound NavT( x t ,y t ) (Pre: At( x s ,y s )) ~ ~ Opt: -At( x s ,y s ), +At( x t ,y t ), -FaceR, +FaceR s t cost ≥ | x s - x t | + | y s - y t | 14

  63. Representing Descriptions: NCSTRIPS • Descriptions specify propositions (possibly) added/deleted by HLA • Also include a cost bound • Can condition on features of initial state NavT( x t ,y t ) (Pre: At( x s ,y s )) ~ ~ Opt: -At( x s ,y s ), +At( x t ,y t ), -FaceR, +FaceR s t cost ≥ | x s - x t | + | y s - y t | Pess: IF Free( x t ,y t ) ∧ ∀ x Free( x,y max ) : s ~ ~ -At( x s ,y s ), +At( x t ,y t ), -FaceR, +FaceR t cost ≤ | x s - x t | + 2 y max - y t - y s + 1 14

  64. Representing Descriptions: NCSTRIPS • Descriptions specify propositions (possibly) added/deleted by HLA • Also include a cost bound • Can condition on features of initial state NavT( x t ,y t ) (Pre: At( x s ,y s )) ~ ~ Opt: -At( x s ,y s ), +At( x t ,y t ), -FaceR, +FaceR s t cost ≥ | x s - x t | + | y s - y t | Pess: IF Free( x t ,y t ) ∧ ∀ x Free( x,y max ) : s ~ ~ -At( x s ,y s ), +At( x t ,y t ), -FaceR, +FaceR t cost ≤ | x s - x t | + 2 y max - y t - y s + 1 x ELSE: s t nil 14

  65. Representing Descriptions: NCSTRIPS • Descriptions specify propositions (possibly) added/deleted by HLA • Also include a cost bound • Can condition on features of initial state • An simple algorithm progresses a valuation (DNF + #) through an NCSTRIPS description to produce next valuation NavT( x t ,y t ) (Pre: At( x s ,y s )) ~ ~ Opt: -At( x s ,y s ), +At( x t ,y t ), -FaceR, +FaceR s t cost ≥ | x s - x t | + | y s - y t | Pess: IF Free( x t ,y t ) ∧ ∀ x Free( x,y max ) : s ~ ~ -At( x s ,y s ), +At( x t ,y t ), -FaceR, +FaceR t cost ≤ | x s - x t | + 2 y max - y t - y s + 1 x ELSE: s t nil 14

  66. Angelic Hierarchical A* (AHA*) • Construct an ALT with the single plan [Act] • Loop • Select a plan with minimal optimistic cost to G • If primitive, return it • Otherwise, refine one of its HLAs • Prune dominated refinements 15

  67. AHA*: Intuitive Picture G s 0 Act highest-level primitive 16

  68. AHA*: Intuitive Picture G s 0 Act highest-level primitive 17

Recommend

THE THE ANGELIC ANGELIC RIGHTS MOVEM RIGHTS MOVEMENT ENT A CLOSER LOOK AT THE WAR IN HEAVEN

THE THE ANGELIC ANGELIC RIGHTS MOVEM RIGHTS MOVEMENT ENT A CLOSER LOOK AT THE WAR IN HEAVEN

THE THE ANGELIC ANGELIC RIGHTS MOVEM RIGHTS MOVEMENT ENT A CLOSER LOOK AT THE WAR IN HEAVEN AND ON EARTH KEY QUESTIONS What does Gods order and government look like? Was Lucifers rebellion in Heaven a rights

402 views • 29 slides
Hierarchical planning 20194524 Jinhyeok Jang A. Online Multilayered Motion Planning with

Hierarchical planning 20194524 Jinhyeok Jang A. Online Multilayered Motion Planning with

Hierarchical planning 20194524 Jinhyeok Jang A. Online Multilayered Motion Planning with Dynamic Constraints for Autonomous Underwater Vehicles, Eduard Vidal et el. ICRA2019 B. Hierarchical Path Planner using Workspace Decomposition and

630 views • 28 slides
Hierarchical Task Network (HTN) Planning Jos Luis Ambite* [* Based in part on presentations by

Hierarchical Task Network (HTN) Planning Jos Luis Ambite* [* Based in part on presentations by

Hierarchical Task Network (HTN) Planning Jos Luis Ambite* [* Based in part on presentations by Dana Nau and Rao Kambhampati] 1 Hierarchical Decomposition 2 Task Reduction 3 Hierarchical Planning Brief History Originally developed about

545 views • 35 slides
Practical Hierarchical Task Network (HTN) Planning Examples of using the core HTN technique along

Practical Hierarchical Task Network (HTN) Planning Examples of using the core HTN technique along

Practical HTN Planning Putting HTN Planning into Use Practical Hierarchical Task Network (HTN) Planning Examples of using the core HTN technique along with other essential planning, search and constraint satisfaction technologies to realise

731 views • 29 slides
Using Hierarchical Modeling to Assist Using Hierarchical Modeling to Assist Effects Based

Using Hierarchical Modeling to Assist Using Hierarchical Modeling to Assist Effects Based

Using Hierarchical Modeling to Assist Using Hierarchical Modeling to Assist Effects Based Planning and Assessment Effects Based Planning and Assessment Lt Col Dave Doc Denhard, PhD Maj Robert Dagwood Umstead Air Force Institute of

791 views • 17 slides
Hierarchical Task Networks Planning to perform tasks rather than to achieve goals 1

Hierarchical Task Networks Planning to perform tasks rather than to achieve goals 1

Hierarchical Task Netw orks Planning to perform tasks rather than to achieve goals Hierarchical Task Networks Planning to perform tasks rather than to achieve goals 1 Literature Malik Ghallab, Dana Nau, and Paolo Traverso. Automated

770 views • 61 slides
Hierarchical Task Network (HTN) Planning Section 11.2 Sec. 11.2 p.1/25 Outline Example

Hierarchical Task Network (HTN) Planning Section 11.2 Sec. 11.2 p.1/25 Outline Example

Hierarchical Task Network (HTN) Planning Section 11.2 Sec. 11.2 p.1/25 Outline Example Primitive vs. non-primitive operators HTN planning algorithm Practical planners Additional references used for the slides: desJardins , M. (2001).

381 views • 25 slides
Hierarchical Task Network (HTN) Planning Section 12.2 Sec. 12.2 p.1/23 Outline Primitive

Hierarchical Task Network (HTN) Planning Section 12.2 Sec. 12.2 p.1/23 Outline Primitive

Hierarchical Task Network (HTN) Planning Section 12.2 Sec. 12.2 p.1/23 Outline Primitive vs. non-primitive operators Example HTN planning algorithm Practical planners Additional references used for the slides: desJardins , M. (2001).

248 views • 23 slides
Hierarchical Planning 2019/11/26 20195062 Jaeyoon Kim Recap. 1. Structural Inspection Path

Hierarchical Planning 2019/11/26 20195062 Jaeyoon Kim Recap. 1. Structural Inspection Path

Hierarchical Planning 2019/11/26 20195062 Jaeyoon Kim Recap. 1. Structural Inspection Path Planning via Iterative Viewpoint Resampling with Application to Aerial Robotics - Minimize redundant viewpoints in terms of 3D recon. 2. Multi-layer

582 views • 24 slides
This is a brief overview of the Hierarchical Task Network planning process. The planner solves a

This is a brief overview of the Hierarchical Task Network planning process. The planner solves a

This is a brief overview of the Hierarchical Task Network planning process. The planner solves a problem by hierarchical decomposition of abstract tasks into concrete actions. The planner is given an abstract task: something that is not complete and

627 views • 35 slides
Exploiting the hierarchical structure of rule-based specifications for decision planning Alberto

Exploiting the hierarchical structure of rule-based specifications for decision planning Alberto

Exploiting the hierarchical structure of rule-based specifications for decision planning Alberto Lluch Artur Boronat Roberto Bruni Ugo Montanari Generoso Paolillo IFIP International Conference on Formal T echniques for Distributed Systems

1.29k views • 94 slides
Hierarchical Multi-Objective Planning For Autonomous Vehicles Alberto Speranzon United

Hierarchical Multi-Objective Planning For Autonomous Vehicles Alberto Speranzon United

Hierarchical Multi-Objective Planning For Autonomous Vehicles Alberto Speranzon United Technologies Research Center UTC Institute for Advanced Systems Engineering Seminar Series This page contains no technical data subject to the EAR or the

507 views • 23 slides
Trajectory planning Trajectory planning 1 1 Basilio Bona 1 ROBOTICA 03CFIOR

Trajectory planning Trajectory planning 1 1 Basilio Bona 1 ROBOTICA 03CFIOR

Trajectory planning Trajectory planning 1 1 Basilio Bona 1 ROBOTICA 03CFIOR Introduction The robot planning problem can be decomposed into a structured class of interconnected activities, at different hierarchical levels, usually

894 views • 54 slides
UT DA Hierarchical and Analytical Hierarchical and Analytical Pla Placement T cement

UT DA Hierarchical and Analytical Hierarchical and Analytical Pla Placement T cement

UT DA Hierarchical and Analytical Hierarchical and Analytical Pla Placement T cement Technique echniques for s for High High- Performa Performance A nce Analog C nalog Circuit ircuits Biying Xu, Shaolan Li, Xiaoqing Xu, Nan Sun, and

506 views • 29 slides
Proposal of a Hierarchical Proposal of a Hierarchical Architecture for Multimodal Architecture for

Proposal of a Hierarchical Proposal of a Hierarchical Architecture for Multimodal Architecture for

Proposal of a Hierarchical Proposal of a Hierarchical Architecture for Multimodal Architecture for Multimodal Interactive Systems y Masahiro Araki* 1 Tsuneo Nitta* 2 Kouichi Katsurada* 2 Takuya Nishimoto* 3 Tetsuo Amakasu* 4 Shinnichi Kawamoto* 5

546 views • 35 slides
Separation and convexity properties of hierarchical and non hierarchical clustering Patrice

Separation and convexity properties of hierarchical and non hierarchical clustering Patrice

Plan Separation and convexity properties of hierarchical and non hierarchical clustering Patrice Bertrand 1 1 CEREMADE, Universit e Paris-Dauphine, Paris, France Joint work with Jean Diatta 2 2 LIM, Universit e de La R eunion,

401 views • 29 slides
HIERARCHICAL DESIGN IN Victor Khomenko, Danil Sokolov, Alex Yakovlev Why hierarchical? The

HIERARCHICAL DESIGN IN Victor Khomenko, Danil Sokolov, Alex Yakovlev Why hierarchical? The

HIERARCHICAL DESIGN IN Victor Khomenko, Danil Sokolov, Alex Yakovlev Why hierarchical? The only way to design large systems Requests from the engineers Natural development of Workcraft Automation of repetitive tasks by tracking

612 views • 13 slides
Preparation for Sonship Mike Parsons 6. Angelic Realm (2) eg.freedomarc.org Preparation for

Preparation for Sonship Mike Parsons 6. Angelic Realm (2) eg.freedomarc.org Preparation for

Preparation for Sonship Mike Parsons 6. Angelic Realm (2) eg.freedomarc.org Preparation for Sonship How can we be prepared for sonship? What types of preparation do we need? Who provides that preparation? Where does that

1.31k views • 52 slides
FeUdal Networks for Hierarchical Reinforcement Learning Alexander Sasha Vezhnevets, Simon

FeUdal Networks for Hierarchical Reinforcement Learning Alexander Sasha Vezhnevets, Simon

FeUdal Networks for Hierarchical Reinforcement Learning Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, Koray Kavukcuoglu Topic: Hierarchical RL Presenter: Thophile Gaudin Why Hierarchical

921 views • 22 slides
Where are we? Informatics 2D Reasoning and Agents Semester 2, 20192020 Last time . . .

Where are we? Informatics 2D Reasoning and Agents Semester 2, 20192020 Last time . . .

Introduction Introduction Execution monitoring and replanning Execution monitoring and replanning Hierarchical Planning Hierarchical Planning Summary Summary Where are we? Informatics 2D Reasoning and Agents Semester 2, 20192020

537 views • 6 slides
Hierarchical Bounding Volume October 11, 2005 () Hierarchical Bounding Volume October 11, 2005

Hierarchical Bounding Volume October 11, 2005 () Hierarchical Bounding Volume October 11, 2005

Hierarchical Bounding Volume October 11, 2005 () Hierarchical Bounding Volume October 11, 2005 1 / 15 Outline Introduction to hierarchical bounding volume (HBV) Tree generation Other optimization issues () Hierarchical Bounding Volume

350 views • 16 slides
Hierarchical Clustering 4-4-16 Hierarchical clustering: the setting Unsupervised learning

Hierarchical Clustering 4-4-16 Hierarchical clustering: the setting Unsupervised learning

Hierarchical Clustering 4-4-16 Hierarchical clustering: the setting Unsupervised learning no labels/output, only x/input Clustering Group similar points together Machine learning taxonomy Supervised Semi-Supervised Unsupervised

366 views • 15 slides
Unsupervised Learning and Clustering Owen Roberts, Zach Busser, Ganesh Sugunan Hierarchical

Unsupervised Learning and Clustering Owen Roberts, Zach Busser, Ganesh Sugunan Hierarchical

Unsupervised Learning and Clustering Owen Roberts, Zach Busser, Ganesh Sugunan Hierarchical Clustering Hierarchical Clustering Hierarchical Representations Hierarchical Algorithms Agglomeration "Nearest" Clusters Complexity

769 views • 49 slides
Hierarchical Modeling Hierarchical modeling has taken over the landscape in contemporaery

Hierarchical Modeling Hierarchical modeling has taken over the landscape in contemporaery

Hierarchical Modeling Hierarchical modeling has taken over the landscape in contemporaery stochastic modeling Intent here is to show a range of examples In the development, we will also show the connections to Gibbs sampling, why Gibbs sampling

340 views • 13 slides

More recommend