Practical htn planning
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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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Practical htn planning

Practical HTN Planning

Putting HTN Planning

into Use


Literature

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


Overview

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


Some planning features

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


More planning features

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


Human approach

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


Htn planning approach

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


Htn activity composition

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


Htn initial plan as goals

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


Nonlin 1974 1977

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


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 <: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


Qa modal truth criterion

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


Qa modal truth criterion1

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


O plan 1983 1999 features

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


O plan 1983 1999 features1

O-Plan (1983-1999) Features

Practical HTN Planning


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

O-Plan Domain Language – TF

?x is a variable

Practical HTN Planning


O plan agent architecture

O-Plan Agent Architecture

Practical HTN Planning


O plan agent architecture1

Later became

Plan Modification Operators

  • Later became

  • Issues

  • Nodes

  • Constraints

  • Annotations

O-Plan Agent Architecture

Practical HTN Planning


O plan planning workflow

O-Plan Planning Workflow

Practical HTN Planning


A more collaborative planning framework

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


I x i plan 2000

I-X/I-Plan (2000- )

  • 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-P2 (I-X Process Panels)

Practical HTN Planning


I n c a framework

<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


I n c a i x

<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


Anatomy of an i x process panel

Anatomy of an I-X Process Panel

Practical HTN Planning


I p 2 aim is a planning workflow and task messaging catch all

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


I x process panel and tools

Domain Editor

Map Tool

Messenger

I-Plan

I-X Process Panel and Tools

Process Panel


I x for emergency response

Central

Authorities

Collaboration and

Communication

Command

Centre

Emergency

Responders

Isolated

Personnel

I-X for Emergency Response


Planning research areas techniques

Planning Research Areas & Techniques

  • Domain ModellingHTN, SIPE

  • Domain DescriptionPDDL, NIST PSL

  • Domain AnalysisTIMS

  • Plan GeneralisationMacrops, EBL

  • Case-Based PlanningCHEF, PRODIGY

  • Plan LearningSOAR, PRODIGY

  • 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

  • User InterfacesSIPE, O-Plan

  • Plan AdviceSRI/Myers

  • Mixed-Initiative PlanSTRIPS/TRAINS

Problem is to make sense of all these techniques

  • Plan GeneralisationMacrops, EBL

  • Case-Based PlanningCHEF, PRODIGY

  • Plan LearningSOAR, PRODIGY

  • Planning Web ServicesO-Plan, SHOP2

  • Plan AnalysisNOAH, Critics

  • Plan SimulationQinetiQ

  • Plan Qualitative MdlingExcalibur

  • Plan Sharing & CommsI-X, <I-N-C-A>

  • NL Generation…

  • Dialogue Management…

  • Plan RepairO-Plan

  • Re-planningO-Plan

  • Plan MonitoringO-Plan, IPEM

Deals with whole

life cycle of plans


Summary

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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