1. Planning, Executing, Sensing, and Replanning for Information Gathering Presented by Hector Gonzalez (Based on slides from Craig Knoblock and L. Joskowicz) Craig Knoblock and L. Joskowicz)

2. Motivation: SIMS Information Mediator Information Mediator “Anywhere” Information Retrieval Decision Support Automatic Planners SIMS Knowledge Bases Local Databases Internet Computer Programs

3. Information Gathering GIVEN: Unifying domain ontology Description of the contents of sources PROBLEM: How to plan and execute information queries many sources, possibly overlapping distributed and heterogeneous environment sources may be unavailable or fail asynchronous queries efficient access is critical

4. The Approach Cast the information gathering task as a planning problem Build on and extend a Partial Order Planner (UCPOP) with: parallel execution interleaved and simultaneous planning and execution asynchronous goals replanning for failures sensing to gather additional information

5. What is a partial order planner?

6. Partial Order Planner: Overview Search through the space of plans. Regression planning: work from goal to start Start from the initial plan, add one step (operator) in each iteration Add only steps that serve to achieve a precondition that has not been achieved yet. Keep track of interactions with causal links. When a conflict occurs, resolve it by imposing an order between steps Keep track of all choice points and backtrack as necessary

7. Example: shopping for groceries SM = Supermarket HWS = Hardware Store Steps: {Start: Op(Action: Start, Effect: At(Home) /\ Sells(HWS,Drill) /\ Sells(SM,Milk) /\ Sells(SM,Banana), Finish: Op(Action: Finish, Precond: At(Home) /\ Have(Drill) /\ Have(Milk) /\ Have(Banana)}

8. Actions: Go and Buy Op(Action: Go(there) Precond: At(here) Effect:At(there) /\ ~ At(here)) Op(Action: Buy(x) Precond: At(store) /\ Sells(store,x) Effect:Have(x) Go(there) At(here) At(there) ~At(here) Buy(x) At(store) Sells(store(x) Have(x)

9. Plan to achieve three preconditions Have(Milk)Have(Ban.) Have(Drill) At(Home) Sells(HWS,Drill) Sells(SM,Milk) Sells(SM,Ban.) Bold links are causal links Light links are ordering links

10. Instantiation and causal links {s/SM} {s/HWS} Causal links can be added because there is no conflict! No ordering is necessary At(Home) Sells(HWS,Drill) Sells(SM,Milk) Sells(SM,Ban.) Have(Milk) Have(Ban.)Have(Drill)

11. Next step: get to the store At(Home) Sells(HWS,Drill) Sells(SM,Milk) Sells(SM,Ban.) At(HWS) ~At(x) At(SM) ~At(x) Have(Milk)Have(Ban.) Have(Drill)

12. Instantiation and causal links {x/Home} At(Home) Sells(HWS,Drill) Sells(SM,Milk) Sells(SM,Ban.) At(HWS) ~At(Home)At(SM) ~At(Home) Have(Milk)Have(Ban.)Have(Drill) Flawed plan! Causal links conflict: cannot be in two places simultaneously ! Re-ordering is necessary

13. Soving causal link conflicts ccc Promotion and demotion sequentialize actions

14. After threat resolution (demotion) At(Home) Sells(HWS,Drill) Sells(SM,Milk) Sells(SM,Ban.) At(HWS)At(SM) Have(Milk)Have(Ban.)At(Home)Have(Drill)

15. Final Solution Have(Milk)Have(Ban.) At(SM) At(Home) Have(Drill) At(HWS) At(Home) Sells(HWS,Drill) Sells(SM,Milk) Sells(SM,Ban.)

16. POP algorithm (1) function POP(initial,goal,operators) returns plan plan := Make-Minimal-Plan(initial,goal) loop do if Solution?(plan) then return plan (S-need,c) := Select-Sub-Goal(plan) Choose-Operator(plan,operators,S-need,c) Resolve-Threats(plan) end function Select-Subgoal(plan) returns (S-need,c) pick a plan step S-need from STEPS( plan) with a precondition c that has not been achieved returns (S-need,c)

17. POP algorithm (2) procedure Choose-Operator(plan,operators,S-need,c) choose (a step S-add from operators) or ( STEPS (plan) that has c as an effect) if there is no such step then fail add causal link (S-add -- c --> S-need) to LINKS (plan) add ordering constraint S-add < S-need to ORDERINGS( plan) if S-add is a newly added step from operators then add S-add to STEPS( plan) add Start < S-add < Finish to ORDERINGS( plan) procedure Resolve-Threats(plan) for each S-threat that threatens a link (Si -- c --> Sj) in LINKS (plan) do choose either Promotion: add S-threat < Si to ORDERINGS (plan) Demotion: add Sj < S-threat to ORDERINGS (plan) if not Consistent(plan) then fail

18. Back to information gathering...

19. Example Query What are the names of all ports that have sufficiently deep channels to accommodate Breakbulk ships  (retrieve (?name) (:and (ship ?ship) (type-name ?ship “breakbulk”) (draft ?ship ?draft) (port ?port) (channel-depth ?port ?depth) (port-name ?port ?name) (< ?draft ?depth)))

20. An Information Goal (available output (retrieve (?name) (:and (ship ?ship) (type-name ?ship “breakbulk”) (draft ?ship ?draft) (port ?port) (channel-depth ?port ?depth) (port-name ?port ?name) (< ?draft ?depth)))) Goal: Description of the data required Desired location Example

21. Information Gathering Operators Operators: Move -- moves data from one server to another Join -- combines two sets of data Translate -- select a source and translate the results (define (operator join) :parameters (?join-op ?data ?data-a ?data-b) :precondition (:and (join-partition ?data ?join-op ?data-a ?data-b) (available local ?data-a) (available local ?data-b)) :effect (available local ?data))

22. Searching the Space of Plans Operators allow only relevant search space e.g., only considers joins across sources Branch-and-bound search expands the lowest-cost plan on each iteration produces the optimal plan Good evaluation function estimates based on size and cardinality information exploits standard database estimation techniques

23. An Example Plan Move From: GEO@server1 To: LOCAL Data: port-name channel-depth Move From: GEO@server1 To: LOCAL Data: port-name channel-depth Move From: ASSETS@server2 To: LOCAL Data: max-draft Move From: ASSETS@server2 To: LOCAL Data: max-draft Translate Source: GEO Translate Source: GEO Translate Source:ASSETS Translate Source:ASSETS Join (< max-draft channel-depth) Join (< max-draft channel-depth) Move From: LOCAL To: OUTPUT Data: port-name Move From: LOCAL To: OUTPUT Data: port-name

24. Planning and Execution in Sage Built on UCPOP Planner [Weld et al.] Support for simultaneous actions [Knoblock ‘94] Interleaving of execution and planning [Ambros-Ingerson ‘87] Planning for asynchronous goals Replanning failed actions Sensing with run-time variables [Etzioni et al., ‘92]

25. Simultaneous Execution Queries to different information sources can be executed simultaneously To support this requires: Explicit resource requirements on actions Adding ordering constraints to avoid conflicts Executing each action as a separate process Unordered actions in a plan can be executed in parallel

26. Simultaneous Execution Planned Executing Move From: GEO@server1 To: LOCAL Data: port-name channel-depth Move From: GEO@server1 To: LOCAL Data: port-name channel-depth Move From: ASSETS@server2 To: LOCAL Data: max-draft Move From: ASSETS@server2 To: LOCAL Data: max-draft Translate Source: GEO Translate Source: GEO Translate Source:ASSETS Translate Source:ASSETS Join (< max-draft channel-depth) Join (< max-draft channel-depth) Move From: LOCAL To: OUTPUT Data: port-name Move From: LOCAL To: OUTPUT Data: port-name

27. Interleaved and Simultaneous Planning and Execution Execution is part of the planning process Each action is either: unexecuted, executing, completed, or failed Planner is done once the action that achieves the goal is completed Executing an action commits to the corresponding plan Delays execution as long as possible Allows planning and execution to be performed simultaneously

28. Planning and Execution Algorithm Select from the possible plans and apply first applicable: Possible conflict? Add possible ordering constraints Unachieved goal? Add possible operators to achieve goal Completed action? Success? Record results Failure? Remove failed actions and update model Pending goal? Add goal to unachieved goals Executable action? Initiate execution and discard alternative plans

29. Planning for Asynchronous Goals Information requests can arise during execution Adds request to the set of top-level goals Augments executing plan to solve new goal Orders actions to avoid conflicts Exploits any overlap

30. Planning for Asynchronous Goals Planned Executing Completed Move From: GEO@server1 To: LOCAL Data: port-name channel-depth Move From: GEO@server1 To: LOCAL Data: port-name channel-depth Move From: ASSETS@server2 To: LOCAL Data: max-draft Move From: ASSETS@server2 To: LOCAL Data: max-draft Translate Source: GEO Translate Source: GEO Translate Source:ASSETS Translate Source:ASSETS Join (< max-draft channel-depth) Join (< max-draft channel-depth) Move From: LOCAL To: OUTPUT Data: port-name Move From: LOCAL To: OUTPUT Data: port-name (retrieve (?desc) (:and (port ?port) (description ?port ?desc) (port-name ?port “Long Beach”)))

31. Planning for Asynchronous Goals Translate Source: GEO Translate Source: GEO Move From: GEO@server2 To: OUTPUT Data: port-description Move From: GEO@server2 To: OUTPUT Data: port-description Planned Executing Completed Move From: GEO@server1 To: LOCAL Data: port-name channel-depth Move From: GEO@server1 To: LOCAL Data: port-name channel-depth Move From: ASSETS@server2 To: LOCAL Data: max-draft Move From: ASSETS@server2 To: LOCAL Data: max-draft Translate Source: GEO Translate Source: GEO Translate Source:ASSETS Translate Source:ASSETS Join (< max-draft channel-depth) Join (< max-draft channel-depth) Move From: LOCAL To: OUTPUT Data: port-name Move From: LOCAL To: OUTPUT Data: port-name

32. Replanning Failed Actions Actions may fail Current state is updated to reflect problem Plan updated to indicate unachieved goals Unachieved goals are then replanned Preserves completed work and work in progress

33. Replanning Failed Actions Planned Executing Completed Failed Translate Source: GEO Translate Source: GEO Move From: GEO@server2 To: OUTPUT Data: port-description Move From: GEO@server2 To: OUTPUT Data: port-description Move From: GEO@server1 To: LOCAL Data: port-name channel-depth Move From: GEO@server1 To: LOCAL Data: port-name channel-depth Move From: ASSETS@server2 To: LOCAL Data: max-draft Move From: ASSETS@server2 To: LOCAL Data: max-draft Translate Source: GEO Translate Source: GEO Translate Source:ASSETS Translate Source:ASSETS Join (< max-draft channel-depth) Join (< max-draft channel-depth) Move From: LOCAL To: OUTPUT Data: port-name Move From: LOCAL To: OUTPUT Data: port-name

34. Replanning Failed Actions Planned Executing Completed Failed Translate Source: GEO Translate Source: GEO Move From: GEO@server2 To: OUTPUT Data: port-description Move From: GEO@server2 To: OUTPUT Data: port-description Move From: ASSETS@server2 To: LOCAL Data: max-draft Move From: ASSETS@server2 To: LOCAL Data: max-draft Translate Source:ASSETS Translate Source:ASSETS Join (< max-draft channel-depth) Join (< max-draft channel-depth) Move From: LOCAL To: OUTPUT Data: port-name Move From: LOCAL To: OUTPUT Data: port-name

35. Replanning Failed Actions Planned Executing Completed Failed Translate Source: GEO Translate Source: GEO Move From: GEO@server2 To: OUTPUT Data: port-description Move From: GEO@server2 To: OUTPUT Data: port-description Move From: GEO@server2 To: LOCAL Data: port-name channel-depth Move From: GEO@server2 To: LOCAL Data: port-name channel-depth Translate Source: GEO Translate Source: GEO Move From: ASSETS@server2 To: LOCAL Data: max-draft Move From: ASSETS@server2 To: LOCAL Data: max-draft Translate Source:ASSETS Translate Source:ASSETS Join (< max-draft channel-depth) Join (< max-draft channel-depth) Move From: LOCAL To: OUTPUT Data: port-name Move From: LOCAL To: OUTPUT Data: port-name

36. Sensing for Information Gathering Sensing actions can be exploited to improve efficiency incorporate sensed data to optimize individual queries gather additional data to narrow down the possibly relevant sources [Knoblock & Levy ‘95] Incorporate run-time variables in the planning language Add sensing goals and actions to the planning operators

37. Sensing for Information Gathering Bind-Result ?draft = ? Bind-Result ?draft = ? use-sensed-data ?draft = ? use-sensed-data ?draft = ? Planned Executing Completed Move From: ASSETS@server2 To: LOCAL Data: max-draft Move From: ASSETS@server2 To: LOCAL Data: max-draft Translate Source: ASSETS Translate Source: ASSETS

38. Sensing for Information Gathering Bind-Result ?draft = 24 Bind-Result ?draft = 24 use-sensed-data ?draft = 24 use-sensed-data ?draft = 24 Planned Executing Completed Move From: ASSETS@server2 To: LOCAL Data: max-draft Move From: ASSETS@server2 To: LOCAL Data: max-draft Translate Source: ASSETS Translate Source: ASSETS

39. Sensing for Information Gathering Bind-Result ?draft = 24 Bind-Result ?draft = 24 use-sensed-data ?draft = 24 use-sensed-data ?draft = 24 Planned Executing Completed Move From: ASSETS@server2 To: LOCAL Data: max-draft Move From: ASSETS@server2 To: LOCAL Data: max-draft Translate Source: GEO Translate Source: GEO Translate Source: ASSETS Translate Source: ASSETS Move From: GEO@server2 To: LOCAL Data: port-name channel-depth > 24

40. Discussion Databases Provides a much more flexible approach than conventional query processors Unlike typical multidatabase systems, does not assume a fixed mapping to the sources Planning Integrates previous work on planning, execution, replanning, and sensing Extends this work to support simultaneous and interleaved planning and execution Demonstrates the ideas in a real application