1. Practical HTN Planning Putting HTN Planning into Use

2. Practical HTN Planning 2 Literature Human Planning Klein, G. (1998) Sources of Power: How People Make Decisions, MIT Press. Refinement Search Kambhampati, S., Knoblock, C.A. and Yang, Q. (1995) Planning as Refinement Search: A Unified Framework for Evaluating Design Tradeoffs in Partial-Order Planning, Artificial Intelligence, Vol. 76, No. 1- 2, pp. 167-238, Elsevier. Nonlin http://www.aiai.ed.ac.uk/project/nonlin/ Tate, A. (1977) Generating Project Networks, Proceedings of the Fifth International Joint Conference on Artificial Intelligence (IJCAI-77) pp. 888-893, Boston, Mass. USA, August 1977. O-Plan http://www.aiai.ed.ac.uk/project/oplan/ Currie, K and Tate, A. (1991) O-Plan: the Open Planning Architecture, Artificial Intelligence Vol. 52, No. 1, pp 49-86, Elsevier. Other Practical Planners Ghallab, M., Nau, D. and Traverso, P., Automated Planning – Theory and Practice, chapters 19, 22 and 23. Elsevier/Morgan Kaufmann, 2004.

3. Practical HTN Planning 3 Overview Human Approaches to Planning Practical HTN Planning Refinement Planning as a Unifying View Nonlin and O-Plan Features QA (Modal Truth Criterion) Time, Resource and Other Constraint Handling I-X/I-Plan Overview

4. Practical HTN Planning 4 Expansion of a high level abstract plan into greater detail where necessary. High level ‘chunks’ of procedural knowledge (Standard Operating Procedures, Best Practice Processes, Tactics Techniques and Procedures, etc.) at a human scale - typically 5-8 actions - can be manipulated within the system. Ability to establish that a feasible plan exists, perhaps for a range of assumptions about the situation, while retaining a high level overview. Analysis of potential interactions as plans are expanded or developed. Identification of problems, flaws and issues with the plan. Deliberative establishment of a space of alternative options, perhaps based on different assumptions about the situation involved, of especial use ahead of time, in training and rehearsal, and to those unfamiliar with the situation or utilising novel equipment. Some Planning Features

5. Practical HTN Planning 5 Monitoring of the execution of events as they are expected to happen within the plan, watching for deviations that indicate a necessity to re-plan (often ahead of this becoming a serious problem). Represent the dynamic state of the world at points in the plan and use this for ‘mental simulation’ of the execution of the plan. Pruning of choices according to given requirements or constraints. Situation dependent option filtering (sometime reducing the choices normally open to one ‘obvious’ one. Satisficing search to find the first suitable plan that meets the essential criteria. Heuristic evaluation and prioritisation of multiple possible choices within the constrained search space. Uniform use of a common plan representation with embedded rationale to improve plan quality, shared understanding, etc. More Planning Features

6. Practical HTN Planning 6 Previous slides describe aspects of problem solving behaviour observed in expert humans working in unusual or crisis situations. Gary Klein, “Sources of Power”, MIT Press, 1998. But they also describe the hierarchical and mixed initiative approach to planning in AI developed over the last 30 years. Human Approach

7. Practical HTN Planning 7 HTN Planning is a useful paradigm… Compose workflows/processes from requirements and component/template libraries Covers simple through to very complex (pre- planned) components Allows for execution support, reactive repair, recovery, etc. Suited to mixed initiative (people and systems) planning and execution Gives an understandable framework within which specialised constraint solvers, domain- specific planners (e.g. route finders), optimisers, plan analysers and simulators can work HTN - Planning Approach

8. Practical HTN Planning 8 A1 A2 A3 A5 A4 “Initial” Plan Refine Introduce activities to achieve preconditions Resolve interactions between conditions and effects Handle constraints (e.g. world state, resource, spatial, etc.) “Final” Plan A2.2 A2.1 A1 A3 A5 A4 Plan Library A2 Refinement S2 S1 HTN - Activity Composition

9. Practical HTN Planning 9 Refine Plan Library Ax Refinement S2 S1 P Initial Plan can be any combination of Activities and Constraints “Refined” Plan A1.2 A1.1 Q P “Initial” Plan P Q HTN – Initial Plan as “Goals”

10. Practical HTN Planning 10 Hierarchical Task Network Planning Partial Order Planner Plan Space Planner Goal structure-based plan development - considers alternative “approaches” only based on plan rationale QA/Modal Truth Criterion Condition Achievement Condition “Types” to limit search “Compute Conditions” for links to external data and systems (attached procedures) Time and Resource Constraint checks Nonlin core is basis for text book descriptions of HTN Planning Nonlin (1974-1977)

11. Practical HTN Planning 11 Nonlin Domain Language – TF opschema makeon pattern {on $*x $*y} expansion 1 goal {cleartop $*x} 2 goal {cleartop $*y} 3 action {put $*x on top of $*y} orderings 1 ---> 3 2 ---> 3 vars x undef y undef; end; opschema makeclear pattern {cleartop $*x} expansion 1 goal {cleartop $*y} 2 action {put $*y on top of $*z} orderings 1 ---> 2 conditions usewhen {on $*y $*x} at 2 usewhen {cleartop $*z} at 2 vars x y undef z :>; end; actschema puton pattern {put $*x on top of $*y} conditions usewhen {cleartop $*x} at self usewhen {cleartop $*y} at self usewhen {on $*x $*z} at self effects + {on $*x $*y} - {cleartop $*y} - {on $*x $*z} + {cleartop $*z} vars x undef y undef z undef; end; always {cleartop table}; initially {on c a} {on a table} {on b table} {cleartop c} {cleartop b} ; plan goal {on a b} goal {on b c}; $*x is a variable “typed” condition restricts search space example of search control knowledge

12. Practical HTN Planning 12 QA in a partially ordered network of nodes Way to establish value of a condition P=V at some point in the plan Yes/no/maybe responses Alternative Terminology: Contributors, deletors (Austin Tate, Nonlin, QA, Edinburgh, 1975-7) White nights and clobberers (David Chapman, MIT, MTC, 1987, 1 st Formalisation) Producers, consumers (Some textbooks) Initially just allowed imposition of orderings on nodes for a condition, a  b (ordering) Later also allowed variables within condition to be constrained – = (codesignation), ≠ (non-codesignation) Intuitively, a white knight is an activity which re-establishes a clobbered precondition p A clobberer in a plan can be "defeated" by imposing ordering or codesignation/non-codesignation constraints on the plan, or by inserting a white knight between the clobberer and the point where a condition is needed QA/Modal Truth Criterion

13. Practical HTN Planning 13 QA/Modal Truth Criterion Before After P=V Contributor No Effect Deletor Need to ensure no deletor appears between a chosen contributor and point of need

14. Practical HTN Planning 14 O-Plan (1983-1999) Features Hierarchical Task Network Planning Nonlin-like goal-structure, QA and Typed/Compute conditions Partial-Plan “Refinement “ Approach Plan State has “flaws”/issues attached Agenda Architecture with Plan Modification Operations “Opportunistic Search” (agenda type, branch1/branch N) Multiple constraint managers with yes/no [and maybe] results Least Commitment Approach (on activity ordering, object/variable bindings and other constraints) Constraint “Posting” rather than explicit commitments (and/or trees with sets of “before” temporal constraints and variable binding (= and ≠) constraints) [as in MOLGEN] Goal structure recording and monitoring to preserve plan rationale

15. Practical HTN Planning 15 O-Plan (1983-1999) Features

16. Practical HTN Planning 16 O-Plan Domain Language – TF types objects = (a b c table), movable_objects = (a b c); always {cleartop table}; schema puton; vars ?x = ?{type movable_objects}, ?y = ?{type objects}, ?z = ?{type objects}; vars_relations ?x /= ?y, ?y /= ?z, ?x /= ?z; expands {puton ?x ?y}; only_use_for_effects {on ?x ?y} = true, {cleartop ?y} = false, {on ?x ?z} = false, {cleartop ?z} = true; conditions only_use_for_query {on ?x ?z} achieve {cleartop ?x} achieve {cleartop ?y}; end_schema; “typed” condition restricts search space example of search control knowledge ?x is a variable

17. Practical HTN Planning 17 O-Plan Agent Architecture

18. Practical HTN Planning 18 O-Plan Agent Architecture Later became Issues Nodes Constraints Annotations Later became Plan Modification Operators

19. Practical HTN Planning 19 O-Plan Planning Workflow

20. Practical HTN Planning 20 Human relatable and presentable objectives, issues, sense-making, advice, multiple options, argumentation, discussions and outline plans for higher levels Detailed planners, search engines, constraint solvers, analyzers and simulators act in this framework in an understandable way to provide feasibility checks, detailed constraints and guidance Sharing of processes and information about process products between humans and systems Current status, context and environment sensitivity Links between informal/unstructured planning, more structured planning and methods for optimisation A More Collaborative Planning Framework

21. Practical HTN Planning 21 Shared, intelligible, easily communicated and extendible conceptual model for objectives, processes, standard operating procedures and plans: IIssues NNodes/Activities CConstraints AAnnotations Communication of dynamic status and presence for agents, and reports about their collaborative processes and process products Context sensitive presentation of options for action Intelligent activity planning, execution, monitoring, re- planning and plan repair via I-Plan and I-P 2 (I-X Process Panels) I-X/I-Plan (2000- )

22. Practical HTN Planning 22 Constraints Issues Nodes Plan State Space of Legitimate Behaviours Issues or Implied Constraints Node Constraints Detailed Constraints I N C A Annotations Framework

23. Practical HTN Planning 23 Constraints Issues Nodes Plan State Space of Legitimate Behaviours Issues or Implied Constraints Node Constraints Detailed Constraints I N C A Annotations & I-X Do (IH) Choose (IH) IH=Issue Handler (Agent Functional Capability) Propagate Constraints

24. Practical HTN Planning 24 Anatomy of an I-X Process Panel

25. Practical HTN Planning 25 I-P 2 aim is a Planning, Workflow and Task Messaging “Catch All” Can take ANY requirement to: Handle an issue Perform an activity Respect a constraint Note an annotation Deals with these via: Manual activity Internal capabilities External capabilities Reroute or delegate to other panels or agents Plan and execute a composite of these capabilities (I-Plan) Receives reports and interprets them to: Understand current status of issues, activities and constraints Understand current world state, especially status of process products Help user control the situation Copes with partial knowledge of processes and organisations

26. Process Panel I-X Process Panel and Tools Domain Editor Messenger I-Plan Map Tool

27. I-X for Emergency Response Collaboration and Communication Command Centre Central Authorities Isolated Personnel Emergency Responders

28. Planning Research Areas & Techniques Problem is to make sense of all these techniques Deals with whole life cycle of plans Search MethodsHeuristics, A* Graph Planning Algthms GraphPlan Partial-Order PlanningNonlin, UCPOP Hierarchical PlanningNOAH, Nonlin, O-Plan Refinement PlanningKambhampati Opportunistic SearchOPM Constraint SatisfactionCSP, OR, TMMS Optimisation MethodsNN, GA, Ant Colony Opt. Issue/Flaw HandlingO-Plan Plan AnalysisNOAH, Critics Plan SimulationQinetiQ Plan Qualitative MdlingExcalibur Plan RepairO-Plan Re-planningO-Plan Plan MonitoringO-Plan, IPEM Plan GeneralisationMacrops, EBL Case-Based PlanningCHEF, PRODIGY Plan LearningSOAR, PRODIGY Plan GeneralisationMacrops, EBL Case-Based PlanningCHEF, PRODIGY Plan LearningSOAR, PRODIGY User InterfacesSIPE, O-Plan Plan AdviceSRI/Myers Mixed-Initiative PlanSTRIPS/TRAINS Planning Web ServicesO-Plan, SHOP2 Plan Sharing & CommsI-X, NL Generation… Dialogue Management… Domain ModellingHTN, SIPE Domain DescriptionPDDL, NIST PSL Domain AnalysisTIMS

29. Practical HTN Planning 29 Summary Human Approaches to Planning Practical HTN Planning Refinement Planning as a Unifying View Nonlin and O-Plan Features QA (Modal Truth Criterion) Time, Resource and Other Constraint Handling I-X/I-Plan Overview