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Practical HTN Planning

Practical HTN Planning. Putting HTN Planning into Use. Literature. Human Planning Klein, G. (1998) Sources of Power: How People Make Decisions, MIT Press. Refinement Search

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Practical HTN Planning

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  1. Practical HTN Planning Putting HTN Planning into Use

  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. 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 Practical HTN Planning

  4. Some Planning Features • 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. Practical HTN Planning

  5. More Planning Features • 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. Practical HTN Planning

  6. Human Approach • 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. Practical HTN Planning

  7. HTN - Planning Approach • 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 Practical HTN Planning

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

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

  10. Nonlin (1974-1977) • 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 Practical HTN Planning

  11. 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 <:non table:> y undef z <:et <:non $*x:> <:non $*y:> :>; 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}; “typed” condition restricts search space example of search control knowledge Nonlin Domain Language – TF $*x is a variable Practical HTN Planning

  12. QA/Modal Truth Criterion • 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, 1st 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 Practical HTN Planning

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

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

  16. 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 O-Plan Domain Language – TF ?x is a variable Practical HTN Planning

  17. O-Plan Agent Architecture Practical HTN Planning

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

  19. O-Plan Planning Workflow Practical HTN Planning

  20. A More CollaborativePlanning Framework • 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 Practical HTN Planning

  21. I-X/I-Plan (2000- ) • Shared, intelligible, easily communicated and extendible conceptual model for objectives, processes, standard operating procedures and plans: • I Issues • N Nodes/Activities • C Constraints • A Annotations • 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-P2 (I-X Process Panels) Practical HTN Planning

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

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

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

  25. I-P2 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 Practical HTN Planning

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

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

  28. Planning Research Areas & Techniques • Domain Modelling HTN, SIPE • Domain Description PDDL, NIST PSL • Domain Analysis TIMS • Plan Generalisation Macrops, EBL • Case-Based Planning CHEF, PRODIGY • Plan Learning SOAR, PRODIGY • Search Methods Heuristics, A* • Graph Planning Algthms GraphPlan • Partial-Order Planning Nonlin, UCPOP • Hierarchical Planning NOAH, Nonlin, O-Plan • Refinement Planning Kambhampati • Opportunistic Search OPM • Constraint Satisfaction CSP, OR, TMMS • Optimisation Methods NN, GA, Ant Colony Opt. • Issue/Flaw Handling O-Plan • User Interfaces SIPE, O-Plan • Plan Advice SRI/Myers • Mixed-Initiative PlanS TRIPS/TRAINS Problem is to make sense of all these techniques • Plan Generalisation Macrops, EBL • Case-Based Planning CHEF, PRODIGY • Plan Learning SOAR, PRODIGY • Planning Web Services O-Plan, SHOP2 • Plan Analysis NOAH, Critics • Plan Simulation QinetiQ • Plan Qualitative Mdling Excalibur • Plan Sharing & Comms I-X, <I-N-C-A> • NL Generation … • Dialogue Management … • Plan Repair O-Plan • Re-planning O-Plan • Plan Monitoring O-Plan, IPEM Deals with whole life cycle of plans

  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 Practical HTN Planning

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