1. GTrans: Mixed-Initiative Planning System by Michael Cox Reviewed by Vu Le IT 803 Spring 2004 – Mixed-Initiative Intelligent Systems – Prof. G. Tecuci

2. 2 Presentation Outline Introduction to GTrans Planning as goal transformation. GTrans: a system using goal transformation Example of using GTrans in planning Details of GTrans Lesson learned References

3. 3 Typical Planning Models: Search In AI community, one of the most dominant models of planning is search, as in Protégé. Planning consists of searching from initial state to goal state via sequence of operators. Pre-cond – Operator(params) – Effect Example: blockworld B C A B C A B C A BCAC B A Initial stateFinal state (Goal)

4. 4 Typical Planning Models: Hierarchical Task Decomposition The other common model is hierarchical task decomposition, as in Disciple. The planning task is successively decomposed into subtasks until they are elementary tasks. Find the solutions for simplest tasks. Successively compose the solutions until solution to the initial task is obtained. S 1 S 11 S 1n S 111 S 11m T 11m T 111 T 1n T 11 T1T1 … …

5. 5 The Alternative Approach: Planning as Goal Transformation The “alternative” is to model planning as goal transformation. Planning involves moving goals through a problem space to reach equilibrium between available resources & constraints of dynamic environment. This approach is still using search in the background and hiding all details of search from user. The user can steer the goal around the problem space with the help from system to find the best possible solutions.

6. 6 Planning as Goal Transformation Equilibrium between resource and constraints: enough resource & constraints are preserved. Constraints violation Goal is to deliver package to a recipient at certain address. Recipient is no longer at that address (constraint violated). Goal change: return the package to sender. Not enough resource Goal is to destroy the bridge. Not enough dynamites to fulfill the task (not enough resource). Goal change: to reduce the transportation through the bridge such as damage part of the bridge.

7. 7 Planning as Goal Transformation The motivation of modeling planning as goal transformation is Cognitive model of planning as search is not intuitive for human because Human planner typically jumps around based on existing knowledge rather than systematically searches the state space. B C A B C A B C A BCAC B A Initial stateFinal state (Goal)

8. 8 Planning as Goal Transformation The other motivation: Being able to understand planning details requires amount of knowledge in planning technology such as pre-conditions, post- conditions and operators with variables bound. Naïve users feel overwhelmed by details of planning as search.

9. 9 Presentation Outline Introduction to GTrans Planning as goal transformation. GTrans: a system using goal transformation Example of using GTrans in planning Details of GTrans Lesson learned References

10. 10 GTrans Mixed-initiative Planning System. Using goal transformation approach. Using Prodigy planning system in backend.

11. 11 Presentation Outline Introduction to GTrans Example of using GTrans in planning Create scenario Step-by-step demonstration Details of GTrans Lesson learned References

12. 12 Create Scenario Create scenario is the first step in planning. Create scenario loads a domain and sets up the objects that belong to domain. Load domain: Military File/LoadDomain/Military: A map comes up with no object. Map has 2 rivers R1, R2 and bridges over them. There is a town & 2 airports. Goal of this exercise is to prevent movement across rivers. When user loads a domain, the system offers the relevant objects in that domain for user to select. Military domain: Objects: fighter jets, warships, missiles…

13. 13 Create Scenario The map has details but they are just images. Objects that represent them are needed to interact with GTrans. Objects have properties: F15 can destroy only 1 bridge and can damage any bridge. Create objects: mobile v.s stationary objects. Mobile objects are drag-and-drop enabled: airplanes, ships, people. Stationary objects are not: bridges, rivers, towns, airports. This scenario needs F15s to destroy bridges. Create 5 bridges over 2 rivers, 4 F15s, 1 police, 1 infantry.

14. 14 Set Initial States Select State/Initial States. Once the user selects initial state, the system suggests lists of initial states associated with objects. User can set the state of objects by right-click on them. Examples: F15_1 is ready Airport_1 is close to river_2 Bridge_1 enables movement over River_1

15. 15 Set Goals Select State/Final States. System helps set up the goals by providing lists of goals associated with objects. There are 2 ways to set goal: Right-click on target, a popup menu with goals associated with objects. Select the desired goal. Bridge: building bridge, destroy bridge. Assign resource such as mobile object to target object by dragging: Right-click on target, a list of goals associated with targets and resources comes up. Select the desired goal. Drag F15 to bridge -> goal: destroy bridge by F15 The goal of this scenario is to make River_1 impassable The letter “G” will be asserted at the target.

16. 16 Run Planner Click on Planning/Run to start the planner. There will be some types of message returned from planner such as: Done or No Plan with explanations. No plan to blow up all 5 bridges with 4 F15s. Need goal transformation. System helps user transform the goal by offering a hierarchical trees of predicates in particular domain. is-destroyed  is-damaged Impassable  restricted-movement.

17. 17 Transform Goal Click on Planning/Change Goals to start the transform. Select current goal and change it. Change from “outcome-impassable” to “outcome-restrict- movement”.

18. 18 The Final Plan

19. 19 Presentation Outline Introduction to GTrans Example of using GTrans in planning Details of GTrans Architecture Communication Control Shared awareness Tasks Evaluation Lesson learned References

20. 20 Architecture RMIKQML Three-layer architecture : GTrans RMI server Planning user interface (PUI) Prodigy/Agent

21. 21 Architecture GTrans RMI server and Planning UI use RMI mechanism to communicate. Planning UI and Prodigy/Agent use KQML to exchange information. GTrans server allows multiple users to co-plan. Planning UI coordinated with Prodigy/Agent allows users to create objects, set or modify goals, solve problems… Prodigy/Agent allows underlying Prodigy to communicate with Planning UIs.

22. 22 GTrans Server Users log in/out to/from GTrans server during planning. A user shares planning information to other users via GTrans server in joint/share planning mode. The user uses PUI to communicate with system. The interactions of Gtrans server and Planning UIs follows publisher-subscribers pattern. A PUI subscribes to the publisher (GTrans server) when user logs in to the system. An event generates from PUI will be distributed to subscribed PUIs via publisher.

23. 23 Planning User Interface (PUI) This is the user’s workplace where menu-based mixed- initiative transactions happen. Users can setup goals, assign resources, change goals with agent assistance. There are two types of transactions from Planning UI To GTrans Server: RMI To Prodigy/Agent: KQML

24. 24 Prodigy/Agent Consists of Prodigy Planner and wrapper Prodigy Planner is a domain independent, state-space planner. Prodigy uses backward & forward search from initial states to goal. Prodigy domains are represented with a conceptual hierarchy and a set of operators. Wrapper serves as interface between Prodigy planner and Planning UI.

25. 25 Presentation Outline Introduction to GTrans Example of using GTrans in planning Details of GTrans Architecture Communication Control Shared awareness Tasks Evaluation Lesson learned References

26. 26 Communication There are 2 types of communication: Among users Between user and agent Communication between users is realized by GTrans RMI server. Communication between user and agent is realized by Prodigy wrapper.

27. 27 Human Users Communication RMI KQML

28. 28 Registration Service GTrans RMI Server offers registration service Each Planning UI connects to GTrans server has to register. Each Planning UI disconnects to GTrans server has to unregister. Registration service monitors all connected clients and allows interaction between clients. Planning UI connects to server via Java RMI.

29. 29 Registration Service GTrans RMI Server Planning UI subscribe acknowledge create event acknowledge distribute event acknowledge unsubscribe Typical events: Add objects Set goals Set states Request shared objects Move objects Delete objects Remove goals Remove states Release shared objects

30. 30 RMI (Remote Method Invocation) RMI facilitates the interprocess communication between Java Virtual Machines. RMI enables remote method invocation. RMI server creates the remote objects. RMI client invokes a method on a remote object. Example Naming.bind(“rmi:///CalendarImpl”, new CalendarImpl()); // create an instance of object and bind it to name CalendarImpl Naming.lookup(“rmi:///cs.gmu.edu/CalendarImpl”); // lookup the remote object at the specified URI: remote objects //bound to the name CalendarImpl at cs.gmu.edu

31. 31 Human User & Agent Communication

32. 32 Human User & Agent Communication The communication is enabled via Prodigy wrapper. KQML (Knowledge Query & Manipulation Language) is used in communication. Lisp-like syntax (tell :content (word “hello”) :sender M :receiver S) (request :content (kill P)) (achieve :content (killed P))

33. 33 KQML - Standard Verbs

34. 34 KQML - Standard Parameters

35. 35 Presentation Outline Introduction to GTrans Example of using GTrans in planning Details of GTrans Architecture Communication Control Shared awareness Tasks Evaluation Lesson learned References

36. 36 Control Issues (Single Plan) User sends planning request to agent via achieve(goals) If the agent does not fully understand the context of the goals, then it asks user via query(obj,state) such as What is initial state of the missile? The clarification is realized by reply(obj,state) If everything is clear then agent generates plan and send back the plan to user via tell(plan) If there are more than one available plans in system then the agent receives a standby signal. In this case we have multiple plans

37. 37 Control Issues (Multiple Plans) When agent receives standby signal then it asks user if s/he wants more by ask-one. The user can cancel or next next signals agent to provide a stream of plans. The first next provides all needed parameters such as depth-bound, time-bound… agent sends back first plan via tell. The next next specifies how the next plan differs from the first (shorter, different…) Continue until no more plan

38. 38 Control Issues (Multiple Plans)

39. 39 Presentation Outline Introduction to GTrans Example of using GTrans in planning Details of GTrans Architecture Communication Control Shared awareness Tasks Evaluation Lesson learned References

40. 40 Shared Awareness There are two types of communication: Among users Between user and agent. Consequently, there are two types of shared awareness: Shared awareness among users Shared awareness between user and agent

41. 41 Shared Awareness Among Users There are 3 modes of planning Separate planning: Plans cannot be seen by other planners, all resultant plans are independent. Information sharing planning: Plans can be seen by other planners, all resultant plans are independent Joint planning: Plans can be seen by other planners, all resultant plans combine into a complex plan. Share/Joint planning allows shared awareness to be realized.

42. 42 Separate Planning The users do not co- operate in separate planning. No one can see other’s plan. The domains from different users can be different. In short, the two plans are total independent.

43. 43 Information Sharing Planning The users do not co- operate in separate planning. But one can see other’s plan. The domains from different users can be different. In short, the two plans are total independent. One of purposes of this mode is to compare plans.

44. 44 Joint Planning The users do co-operate in planning. One can see other’s plan. The domains from different users can be different. In short, the two planning are independent. Joint planning is really useful in multi-domains planning.

45. 45 Joint Planning Example of joint planning: Suppose to send a package from a post office to an oversea country. One planner has expertise in truck-logistics domain. The other planner has expertise in air-logistics domain. Neither domain is suffice to carry on the task. Neither planner can achieve the goal by itself. Need to joint plan: from post office carry package by truck to nearest airport. From there fly the package over to that country.

46. 46 Shared Objects Ownership of objects: in joint planning the server serves as repository of objects. If user wants to borrow objects not owned by her/him then s/he sends request to server. If the object is available (either not owned by any one or owned but not in use) then that object is sharable. Example: In military domain, an F15 is defined as being able to blow up only 1 bridge but can damage a numbers of bridges. If the task is blowing up 2 bridges with only 1 F15 then there are 2 solutions: Either ask for help by getting 1 more F15 if it’s available. Or reduce the goal to blow up 1 bridge and damage 1 bridge.

47. 47 Shared Awareness between user and agent The agent and user share information during the discourse. The agent sends to user the info based on request: All objects in domain. All available goals in domain. All initial states in domain. All hierarchy of concepts, predicates in domain. The information helps user create objects, set initial states, setup goals, transform goals.

48. 48 Shared Awareness between user and agent All objects in domain: help user select available objects in domains. Fighter jets, warships, missiles… in military domain. Available goals in domain: help user decide what goal to achieve. Associated with bridge is goal of destroying, building… Initial states in domain: help user setup initial states of problem. Associated with missiles is state of being stored, loaded… Hierarchy of concepts, predicates: help user transform the goal. Hierarchy of predicates: is-destroyed  is-damaged

49. 49 Presentation Outline Introduction to GTrans Example of using GTrans in planning Details of GTrans Architecture Communication Control Shared awareness Tasks Evaluation Lesson learned References

50. 50 Task: User Task The planning details are hidden from user in black-box. The user has 3 handles to the black-box. The user controls the planning process by: Defining the goals. Assigning the resources. This is nice feature of Goal Transformation. Users has more control over the process. Set the priorities of goals (not implemented). Adjusting the goals and resources based on the world changes as well as the response (succeeded, failed with explanations) from black-box.

51. 51 Task: Agent Task Generates the plan based on inputs from users. Helps users create objects, setup initial states and goals (via menus) Helps users transform the goals. There are 3 types of goal transformation: Goal type transformation Goal argument transformation Negated goal transformation

52. 52 Goal Type Transformation Transform a goal by moving the predicate of the goal along an abstraction hierarchy defined in the domain knowledge. is-ineffective is-isolatedis-destroyed (is-ineffective enemy-brigade1) (is-isolated enemy-brigade1) or (is-destroyed enemy-brigade1) is more specific. Assume the initial plan is (is-destroyed enemy-brigade1) The discovery of non-combatants in the battle area necessitates the change to (is-isolated enemy-brigade1)

53. 53 Goal Argument Transformation Transform a goal by moving its arguments along the abstraction hierarchy of objects in domain knowledge. Initial goal: (destroyed-by bridge_1 F15_1) The specific bridge bridge_1 is destroyed by specific F15_1 Transformed goal: (destroyed-by bridge F15) Any bridge can be destroyed by any F15. The transformed goal is more generic than initial goal. When there is not enough resource to fulfill initial goal, the transformed goal is good enough.

54. 54 Negated Goal Transformation This type of transformation is to inverse a goal. Purposes: for what-if scenario The user may want to see what happens if bridge_1 is not destroyed by f15_1 Or to annul a particular goal and still want to keep the planning process going on. Initial goal: destroy bridge_1 and bridge_2 with only 1 F15_1. => Plan failed Transformed goal: destroy bridge_2 only (more critical infrastructure).

55. 55 Presentation Outline Introduction to GTrans Example of using GTrans in planning Details of GTrans Architecture Communication Control Shared awareness Tasks Evaluation Lesson learned References

56. 56 Goal Satisfaction as Function of Cognitive Model Goal satisfaction is the ratio of actual goal satisfaction achieved by the participant’s plan to the goal satisfaction achieved by the optimal plan. Given a goal to destroy a bridge, destroying it achieves 100% goal satisfaction, damaging it achieves only 50% goal satisfaction. The experiment uses 18 variations on the Bridge problem. Insufficient resources exists. Uses 2 models: search and goal transformation. Search model allows partial goal planning. Goal manipulation lets users transform the goal to achieve partial goal planning.

57. 57 Goal Satisfaction as Function of Cognitive Model Experts are 6 out of 13 subjects who had familiarity to search in AI. When presents with goal manipulation model, achievement is over 95 percent goal satisfaction on average. When presents with search model, achievement is about 80 percent goal satisfaction on average.

58. 58 Goal Satisfaction as Function of Complexity Problems with longer plan is considered more complex than problem with shorter plan.  Complexity is dependent on the number of steps in the optimal plan. Uses the same setup as before. Categorizes 18 problems into 3 categories: Number of steps ≤ 13: Easy 14 ≤ Number of steps ≤ 25: Medium 26 ≤ Number of steps: Hard

59. 59 Goal Satisfaction as Function of Complexity When the goal manipulation model is presented to users, the goal satisfaction almost remains the same with increasing problem complexity. When the search model is presented to users, the goal satisfaction decreases when the problem complexity increases. Presents the average goal satisfaction ratio for combination of planning model and problem complexity.

60. 60 Goal Satisfaction as Function of Expertise Using the same setup as before. Presents the average goal satisfaction ratio for combination of planning model and expertise levels. The experts perform better than the novices in each model. The goal satisfaction ratio is still higher in goal manipulation model. Both lines are not parallel and that means the difference between novices and experts in goal manipulation model is less than that of search model.

61. 61 Presentation Outline Introduction to GTrans Example of using GTrans in planning Details of GTrans Lesson learned References

62. 62 Lesson Learned Strength: Planning becomes easier with visualization. Hiding all search trees from novices => easier for them to make decision. Assigning resources & transforming goals as needed make planning more understandable. System helps users in setting up scenario and goals as well as transforming the goal. Drag & Drop capability speeds up the planning process.

63. 63 Lesson Learned Weakness: All goals have the same priorities. In Joint/Share planning modes, all information is shared. Should share only information which may interfere the planning. Mixed-initiative is less interactive and less explicit. No appropriate explanation for a planning failure. No learning capability.

64. 64 Presentation Outline Introduction to GTrans Example of using GTrans in planning Details of GTrans Lesson learned References

65. 65 References Cox, M. T., Edwin, G., Balasubramanian, K., & Elahi, M. (2001). Multiagent goal transformation and mixed- initiative planning using Prodigy/Agent. To appear in Proceedings of the 5th World Multiconference on Systemics, Cybernetics and Informatics (SCI 2001). http://www.cs.wright.edu/~mcox/Papers/cox-edwin- balas-elahi.pshttp://www.cs.wright.edu/~mcox/Papers/cox-edwin- balas-elahi.ps Cox, M., Kerkez, B., Srinivas, C., Edwin, G., Archer, W. (2000). Toward Agent-Based Mixed-Initiative Interfaces.In H. R. Arabnia (Ed.), Proceedings of the 2000 International Conference on Artificial Intelligence, Vol. 1 http://www.cs.wright.edu/people/faculty/mcox/ic-ai/abmii-camera-ready.pdfhttp://www.cs.wright.edu/people/faculty/mcox/ic-ai/abmii-camera-ready.pdf Immaneni, T., & Cox, M. T. (2004). GTrans: An application for mixed-initiative collaborative planning during emergency response situations. In W. W. Smari & W. McQuay (Eds.), Proceedings of the 2004 International Symposium on Collaborative Technologies and Systems (CTS 04), (pp. 121-126). San Diego: Society of Modeling and Simulation International. http://www.cs.wright.edu/people/faculty/mcox/Papers/immaneni-cox- 04.pdfhttp://www.cs.wright.edu/people/faculty/mcox/Papers/immaneni-cox- 04.pdf Veloso, M. M., Mulvehill, A. M., & Cox, M. T. (1997). Rationale-supported mixed-initiative case-based planning. In Proceedings of the Fourteenth National Conference on Artificial Intelligence and Ninth Innovative Applications of Artificial Intelligence Conference (pp. 1072-1077). Menlo Park, CA: AAAI Press / The MIT Press. http://www.cs.wright.edu/~mcox/Ftp/veloso-mulvehill-cox.pshttp://www.cs.wright.edu/~mcox/Ftp/veloso-mulvehill-cox.ps Zhang, C., Cox, M. T., & Immaneni, T. (2002). GTrans version 2.1 User manual and reference (Tech. Rep. No. WSU-CS-02-02). Dayton, OH: Wright State University, Departmentof Computer Science and Engineering. http://www.cs.wright.edu/~mcox/Gtrans/Tech-Rep-WSU-CS-02-02.pdf http://www.cs.wright.edu/~mcox/Gtrans/Tech-Rep-WSU-CS-02-02.pdf Cox, M. T. (2001). Toward tailored information presentation in support of collaborative planning. In B. Bell & E. Santos (Eds.), Intent Inference for Collaborative Tasks: Papers from the 2001 fall symposium (pp. 44-50). AAAI Technical Report FS-01-05.Menlo Park, CA :AAAI Press. http://www.cs.wright.edu/~mcox/Ftp/mcox01.pdf http://www.cs.wright.edu/~mcox/Ftp/mcox01.pdf Cox,M.T.(2000).A conflict of metaphors :Modeling the planning process.In Proceedings of 2000 Summer Computer Simulation Conference (pp. 666-671).San Diego :The Society for Computer Simulation International. http://www.cs.wright.edu/people/faculty/mcox/Ftp/metapho-camera-ready.pdfhttp://www.cs.wright.edu/people/faculty/mcox/Ftp/metapho-camera-ready.pdf Finin, T., McKay, D., & Fritzson, R. (1992). An Overview of KQML: A Knowledge Query and Manipulation Language. Computer Science Department, University of Maryland. http://citeseer.nj.nec.com/chalupsky92overview.html http://citeseer.nj.nec.com/chalupsky92overview.html Zhang, C. (2002), Cognitive Models For Mixed-Initiative Planning. Master’s thesis. Wright State University, Dayton, OH. http://www.cs.wright.edu/~mcox/GTrans/zhang2002.pdfhttp://www.cs.wright.edu/~mcox/GTrans/zhang2002.pdf

66. 66 References Cox, M. T. (in press). Planning as mixed-initiative goal manipulation. To appear in the Proceedings of the Workshop on Mixed-Initiative Intelligent Systems at the 18th International Conference on Artificial Intelligence. Menlo Park, CA: AAAI Press. http://www.cs.wright.edu/~mcox/Ftp/cox-ijcai03.pdfhttp://www.cs.wright.edu/~mcox/Ftp/cox-ijcai03.pdf Michael T. Cox, Thomas Hartrum, Scott DeLoach,1 and S. Narayanan. Agent-Based Mixed-Initiative Collaboration: The ABMIC project final report. Wright State University Technical Report WSU-CS-02-01, July 2002. http://www.cis.ksu.edu/~sdeloach/ai/publications/ABMIC-final.report2.pdfhttp://www.cis.ksu.edu/~sdeloach/ai/publications/ABMIC-final.report2.pdf Cox, M. T., & Veloso, M. M. (1997a). Controlling for unexpected goals when planning in a mixed-initiative setting. In E. Costa & A. Cardoso (Eds.), Progress in Artificial Intelligence: Eighth Portuguese Conference on Artificial Intelligence (pp. 309-318). Berlin: Springer. http://www- 2.cs.cmu.edu/afs/cs.cmu.edu/project/prodigy/Web/Mixed-init/Papers/m-init3.htmlhttp://www- 2.cs.cmu.edu/afs/cs.cmu.edu/project/prodigy/Web/Mixed-init/Papers/m-init3.html Cox, M. T., & Veloso, M. M. (1997b). Supporting combined human and machine planning: An interface for planning by analogical reasoning. In D. B. Leake & E. Plaza (Eds.), Case-Based Reasoning Research and Development: Second International Conference on Case-Based Reasoning (pp. 531-540). Berlin: Springer- Verlag. http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/prodigy/Web/Mixed-init/Papers/ui.ps.gzhttp://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/prodigy/Web/Mixed-init/Papers/ui.ps.gz Cox, M. T., & Veloso, M. M. (1997c). Supporting combined human and machine planning: The Prodigy 4.0 User Interface Version 2.0 (Tech, Rep. No. CMU-CS-97-174). Pittsburgh: Carnegie Mellon University, Computer Science Department. http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/prodigy/Web/Mixed- init/Papers/ui-tech-rep.ps.gzhttp://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/prodigy/Web/Mixed- init/Papers/ui-tech-rep.ps.gz Cox, M. T., & Veloso, M. M. (in press). Goal Transformations in Continuous Planning. In M. desJardins (Ed.), Proceedings of the 1998 AAAI Fall Symposium on Distributed Continual Planning. Menlo Park, CA: AAAI Press / The MIT Press. http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/prodigy/Web/Mixed- init/Papers/goal-trans.ps.gzhttp://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/prodigy/Web/Mixed- init/Papers/goal-trans.ps.gz Tecuci, G. Lecture notes of CS785 George Mason University, 2001. Stanescu, B. Presentation: The Trips system. George Mason University, 2004.