practical htn planning putting htn planning into use
TRANSCRIPT
Practical HTN Planning
Putting HTN Planning
into Use
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.
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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
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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
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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
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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
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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
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A1
A2
A3
A5A4
“Initial” Plan
Refine
Introduce activities to achieve preconditionsResolve interactions between conditions and effects
Handle constraints (e.g. world state, resource, spatial, etc.)
“Final” Plan
A2.2A2.1
A1
A3
A5A4
Plan Library
A2 Refinement
S2S1
HTN - Activity Composition
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Refine
Plan Library
Ax Refinement
S2S1
P
Initial Plan can be any combination of Activities and Constraints
“Refined” Plan
A1.2A1.1
Q
P“Initial” Plan
P
Q
HTN – Initial Plan as “Goals”
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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)
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Nonlin Domain Language – TFopschema 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 $*z} vars x undef y undef z undef;end;
always {cleartop table};
initially {on c a} {on a table} {on b table} {cleartop c} {cleartop b} ;
plan goal {on a b} goal {on b c};$*x is a variable
“typed” condition restricts search spaceexample of search control knowledge
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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
QA/Modal Truth Criterion
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QA/Modal Truth Criterion
Before After
P=V
Contributor
No Effect
Deletor
Need to ensure no deletorappears between a chosencontributor and point of need
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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
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O-Plan (1983-1999) Features
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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 spaceexample of search control knowledge
?x is a variable
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O-Plan Agent Architecture
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O-Plan Agent Architecture
Later became• Issues• Nodes• Constraints• Annotations
Later becamePlan
Modification Operators
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O-Plan Planning Workflow
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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 CollaborativePlanning Framework
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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)
I-X/I-Plan (2000- )
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Constraints
Issues
Nodes
Plan State
Space of Legitimate Behaviours
Issues or ImpliedConstraints
NodeConstraints
DetailedConstraints
I
N
C
A Annotations
<I-N-C-A> Framework
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Constraints
Issues
Nodes
Plan State
Space of Legitimate Behaviours
Issues or ImpliedConstraints
NodeConstraints
DetailedConstraints
I
N
C
A Annotations
<I-N-C-A> & I-X
Do (IH)
Choose (IH)
IH=Issue Handler (Agent Functional Capability)
PropagateConstraints
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Anatomy of an I-X Process Panel
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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
Process Panel
I-X Process Panel and Tools
Domain Editor
Messenger I-Plan
Map Tool
I-X for Emergency Response
Collaboration and Communication
CommandCentre
CentralAuthorities
IsolatedPersonnel
EmergencyResponders
Planning Research Areas & TechniquesPlanning Research Areas & Techniques
Problem is to
make sense
of all t
hese techniques
Problem is to
make sense
of all t
hese techniques
Deals with whole
life cycle of plans
• 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
• Plan Analysis NOAH, Critics• Plan Simulation QinetiQ• Plan Qualitative MdlingExcalibur
• Plan Repair O-Plan• Re-planning O-Plan• Plan Monitoring O-Plan, IPEM
• Plan Generalisation Macrops, EBL• Case-Based Planning CHEF, PRODIGY• Plan Learning SOAR, PRODIGY
• Plan Generalisation Macrops, EBL• Case-Based Planning CHEF, PRODIGY• Plan Learning SOAR, PRODIGY
• User Interfaces SIPE, O-Plan• Plan Advice SRI/Myers• Mixed-Initiative PlanS TRIPS/TRAINS
• Planning Web Services O-Plan, SHOP2
• Plan Sharing & Comms I-X, <I-N-C-A>• NL Generation …• Dialogue Management …
• Domain Modelling HTN, SIPE
• Domain Description PDDL, NIST PSL
• Domain Analysis TIMS
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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