application of artificial intelligent planning · ai planning is ubiquitous in on a daily basis...

OutlineIntroduction

Where AI Planning usedProperties of AI Planning

Types of Planning Problems and AlgorithmsNon-Linear Planning

Application of AI PlanningCurrent research in AI Planning

Conclusion and SummaryReferences

Application of Artificial Intelligent Planning

byKomminist (04305201)

Somasekara Rao (05407008)

November 13, 2005

by Komminist (04305201) Somasekara Rao (05407008) Application of Artificial Intelligent Planning

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Motivation

AI Planning is ubiquitous in on a daily basis life, for example, fromplanning how to make dinner to planning how to graduate fromUniversity with the least amount of work. Researchers in AIcommunity have studied AI planning problems for many decades,and many techniques exist for automating planning processes.This seminar explore both conventional and modern approaches toAI planning. Issues to be discussed include: how to representactions and world state, how to search for plans efficiently, andmore .. by the help of example(s)


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Outline

Introduction

Need of AI Planning

Properties of AI Planning

Types of Planning Problem and Algorithms

Planning in Application

Summary and Conclusions


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What is AI Planning?

Definition Of AI planning

...Planning every where ...

Planning is a problem solving technique.Planning is reasoning about future events in order to verify theexistence of a reasonable series of actions to take in order toaccomplish a goal.

... we do planning ...

to reduce searching,to resolve conflicts, andto provide basis for error recovery.


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AI Planning ...

In more general sense : Given

a way to describe the worldan initial state of the worlda set of goals to achieveda set of possible action to change the world

FindA prescription for actions to change the initial state into onethat fulfill the goal


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Representation of Change ...

As actions change the world or we consider possible actions,we want to :

Know how an action change the world ,Keep track of the history of the world states (have we beenhere before?)Answer questions about potential world states (what wouldhappened if...?)


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Planning domains ...

Web management agents :

An Efficient Plan Execution System for InformationManagement Agents for gathering information from the WorldWide Web ...

Robot planning

For controlling the legs, arms and action of the robot (perhapsmost of planning research relay here!)

Manufacturing planning, Image processing management,Linguistic transportation


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Domain Cond ...

Crisis management , Bank risk management, Blocks-world,

Puzzles, Artificial Domain,

Software planning ( postponed example later ) and more ...


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Properties of Planning Algorithms

SoundnessA planning algorithm is sound if all solutions found are legalplans

All preconditions and goals are satisfiedNo constraints are violated (temporal, variable binding)

CompletenessA planning algorithm is complete if a solution can be foundwhenever one actually existsA planning algorithm is strictly complete if all solutions areincluded in the search space

OptimalityA planning algorithm is optimal if the order in which solutionsare found is consistent with some measure of plan quality


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Types of planningLinear PlanningPlanning as SearchDeterministic vs Non deterministic

Types of Planning

Linear planning - Planning with a goal stack.

Work on one goal until completely solved before moving on tothe next goalMore feasible if zero goal interaction !

Nonlinear planning - Handles goal interactions byInterleaving.

Use goal set instead of goal stackInclude in the search space all possible subgoal orderings


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Linear planning : discussion ...

Basic Idea

Work on one goal until completely solved before moving on tothe next goal

Planning algorithm maintains goal Stack

Implications

No interleaving of goal achievementEfficient search if goals do not interact much !!!


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Means-Ends Analysis

Basic Idea

Search only relevant aspects of problemWhat means (operators) are available to achieve the desiredends goal

Find difference between goal and current state

Find operator to reduce difference

Perform means-ends analysis on new subgoals ...

Used often in Agent-Oriented Software Modellinglanguage, such as i* , Tropos,...


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GPS (Newell, Simon, Ernst, 1960s)

Introduced concept of means-ends analysis

Essentially linear planning using recursive procedure calls asthe goal-stack mechanism

Simple and easy as well as the first planning algorithm .


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STRIPS (Fikes, Nilsson, 1971)

Same basic idea as GPS, but

Solved the frame problem (STRIPS assumptions)

Actions must specify all the state variables whose values theychange...No disjunction allowed in effects and like that ...

Introduced operator representation

Operationalized notion of difference, subgoals, and operatorapplication

Dealt (somewhat) with plan execution and learning.


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Pros and Cons on Linearity

Advantages

Reduced search space, since goals are solved one at a time.Advantageous if goals are (mainly) independentLinear planning is sound

Disadvantages

Linear planning may produce suboptimal solutions (based onthe number of operators in the plan)Linear planning is incomplete.


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Example : One-way Rocket

(Op :LOADprecAt(obj,loc)At(roc,loc)effectsadd : inside(obj,roc)del : At(obj,loc)

Op : UNLOADpreinside(obj,roc)At(roc,loc)effectsadd : At(obj,loc)del : inside(obj, roc)

Op : MOVEpreAt(roc,from)has-fuel(roc)effectsadd : At(roc,to)del : At(roc,from),del : has-fuel(roc)


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Incompleteness of Linear Planning

Initial state :At(obj1,locA)At(obj2, locA)At(roc, locA)has-fuel(roc)

Goal Statement:( and At(obj1,locB)At(obj2, locB )

Goal Plan

At(obj1,locB) LOAD-ROCKET obj1 locA)(MOVE-ROCKET)(UNLOAD-ROCKET obj1 locb)

At(obj2,locB)failure


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State-Space Search

Node denoted a state of the world.

Arcs corresponds to the executionof a specific action

Planning problem : finding a path.

Difficulty : requires CompleteDescription of the states searched,and requires the search to becarried out locally

Can be progression or regression ...


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Plan-space search

Each node in the graph representspartial plans.

Arcs denote plan refinementoperations.

One can search for a plan : totalorder planning, or partial orderplanning.


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Partial Order Planning

Basic Idea :

Search in plan space and use least commitment, when possible

Initial Plan :

< {start, finish}, {start < finish, {}}start has preconditions; Its effects are the initial statefinish has no effects; Its preconditions are the goals


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POP contd ...

A set of actions : For example,{eat-breakfast (go to mess) , take-shower, wake-up, go-to-AISeminar }

A set of ordering constraints : For example,{wake-up before eat-breakfast, wake-up before take-shower,wake-up before go-to-AI Seminar, take-shower beforego-to-work}

A set of causal links ( set of bindings ) : For example,wake-up −−−awake−−− >eat-breakfastis a link from the action, wake-up to the action eat-breakfast.Note : When the action wake-up is added to the plan, theabove causal link is recorded, along with the orderingconstraint {wake-up before eat-breakfast}


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Least Commitment

Basic IdeaMake choices only that are relevant to solving the current partof the problem

Least commitment choicesOrderings : Leave actions unordered, unless they must besequentialBindings : Leave variables unbounded, unless needed to unifywith conditions being achieved.Actions : Usually not subjected to ”least commitment”

RefinementOnly add information to the current planTransformational planning can remove choices


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Many more non-linear planning

Graph-based searchSat-based searchOBDD-based search

Hierarchical planningEmphasis on action decomposition/refinementKnowledge engineering/acquisitionVery little search

many more ...


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Classical Deterministic planning

State of the world at any moment is unambiguouslydetermined by the initial state of the world and sequence ofactions that have been taken.

Action Model : Complete, deterministic, correct, richrepresentation

State : single initial state, fully known

Goals : complete satisfaction


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Deterministic planning contd ...

Given set of states = black dots,actions = (blue, red) and initialstate = I and a set of goal states= G

The task is to find the a pathfrom initial state to one of thegoal states........


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Non-deterministic Planning

The world is not always deterministic in nature !!! Why ...?

At least for two reasons:The model of the World is usually very incomplete !

We do not know whether somebody is going to phone or visitus, and then the visit or phone call can be modeled as anondeterministic event that may or may not take place.

Second, many actions themselves are by their naturenondeterministic, either intentionally or unintentionally.

Throwing two dice has 12 possible outcomes that usuallycannot be predicted (which is why throwing dice isinteresting!)Throwing some object to a garbage bin from a distance mayor may not succeed Try it after this !!!


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Non-deterministic contd ...

we added a red arrow to thesecond state above the initialstate,

as well as a new state to thebottom of right corner with anondeterministic transitionleading to it.

the new arrows make the redaction nondeterministic, and animpact on which action sequencesare plans.


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State - Space Nonlinear Planning

Extend linear planningUse goal set instead of goal stackInclude in the search space all possible subgoal orderingsHandles goal interactions by interleaving

AdvantagesNon-linear planning is soundNon-linear planning is completeNon-linear planning may be optimal with respect to planlength (depending on search strategy employed)

DisadvantagesLarger search space, since all possible goal orderings may haveto be consideredSomewhat more complex algorithm; More bookkeeping


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Completeness of State-Space Nonlinear Planning

State-space nonlinear planning is Complete( Continued from the incompleteness of the linear planning ...)

Goal Plan

At(obj1,locB) (LOAD obj1 locA)

At(obj2,locB) (LOAD obj1 locA)

At(obj1,locB) (MOVE)(ULOAD obj1 locB)

At(obj2,locB) (ULOAD obj2 locB)


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Generation a Solution plan

A complex process :

Alternative operators to achieve a goal.Multiple goals that interactsEfficiency and plan quality.


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More Planning under nonlinear

NOAM [Sacerdoti1975]

First nonlinear, partial-order plannerIntroduce the notion of plan-space searchUsed TOME (Table Of Multiple Effects) to detect goalinteractions

SNLP (Systematic Non Linear Planner), UCPOP (UniversalConditional POP )

SHOP2 ( Simple Hierarchical Order Planner )

SAVTA (Subgoal After Every Try to Apply), SABA ( SubgoalAlways Before Applying)


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Applying AI Planning for SEAI Planning for Tool construction

Planning for AOSE : Motivation to i*

i* : graphical modeling language used in AOSE (tropos)

i* => a framework for modeling social settings, based on thenotions of actors,goals, dependencies ( social interactions )...

i* framework consists of two graphical models: SD and SR

The SD diagram is used to represents the strategicdependencies.

Which express intentional relationships that exist amongactors in order to fulfill some strategic objectives.

Dependency type : goal Dependency, Softgoal Dependency,Task Dependency,Resource Dependency


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SD : for the CAS


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SR Model: b/n Student and RAO

SR provides a more detailed level of modeling than SD bylooking in-side actor.


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So where is AI planning used?

Goal decomposition/refinment ( Conjuction and /ordisjuction) ...

GetAdmission : (Apply, PassExam, GetAdmResult)

Ordering (partial or total) should has to consider (prior-to):For example

Apply is possible iff you have pass exam !!

Imagin that there is high goal interaction among actors viadependencies !!

To get admission a student needs admission co-ordinator whichis the RAO

So here is the challenge!! But carefully choose your planner!


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Example-I

Initial State Set Of Objects

part1, type: PART

drill-1, type: SPOT-DRILL

drill-2, type: TWIST-DRILL

Goal Statement

has-hole(part)by Komminist (04305201) Somasekara Rao (05407008) Application of Artificial Intelligent Planning

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Example-I Contd..

drill-spot(part, drill-bit)Pre: (holding-tool drill-bit)

(holding-part part)Add: (has-spot part)

drill-hole(part, drill-bit)Pre: (has-spot part)

(holding-tool drill-bit)(holding-part part)

Add: (has-hole part)

put-drill-bit(drill-bit)Pre: (tool-holder-empty)Add: (holding-tool drill-bit)Del: tool-holder-empty

remove-drill-bit(drill-bit)Pre: (holding-tool drill-bit)Add: tool-holder-emptyDel: (holding-tool drill-bit)

put-part(part)Pre: part-holder-emptyAdd: (holding-part part)Del: part-holder-empty

remove-part(part)Pre: (holding-part part)Add: part-holder-emptyDel: (holding-part part)by Komminist (04305201) Somasekara Rao (05407008) Application of Artificial Intelligent Planning

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Example-I contd... sequence of actions

put-part(part)put-drill-bit(drill-1)drill-spot(part drill-1)remove-drill-bit(drill-1)put-drill-bit(drill-2)drill-hole(part drill2)


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Applications : Current and Potential

Scheduling problems with action choices as well as resourcehandling requirements

Problems in supply chain managementHSTS (Hubble Space Telescope scheduler)Workflow management

Autonomous agentsRAX/PS (The NASA Deep Space planning agent)

Software module integratorsTest case generation (Pittsburgh)

Interactive decision supportMonitoring subgoal interactions

Plan-based interfaces


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Current research direction

Planning application in Robotics

Planning for web-service composition

Planning databasesDistributed Intelligent Agents Group: For example

Multiagent Planning and Coordination as Distributed SearchMultiagent Planning and Coordination for Unmanned GroundVehiclesIntelligent Agent Infrastructures for Supporting Collaborationmore description =>http://ai.eecs.umich.edu/diag/homepage.html

AI Planning in Grid Computing

AI planning in Tropos Methodology and more .....


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Conclusion drawn

No general planning algorithm

Tackling real-world planning problems often requiresconsidering various types of constraints, ranging from simplenumerical comparators to complex resources.

There is a method how to solve planning tasks within generalconstraint-solving frameworks, such as propositionalsatisfiability, integer programming, and constraintprogramming. ”paper : Constraints and AI Planning”

Planning as search : control rules to capture heuristics forefficient search; learning opportunities!


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Conclusion contd ...

Applying AI planning to Early-phase of SE ensures quality ofSoftware Development in both functional and non-functionalaspect of the system.

For example, Tropos Software engineering use planning forsearching of interactive goal in the specification to handleconsistencies among different actors hence which is moreamendable for formal verification and specification ofsoftwares.

Every body has to practice planning to become rich ! (Please do planning now onwards!!)


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References and Links

AIPS 2002 Sixth International conference on ArtificialIntelligence Planning and Scheduling (AIPS2002) Workshopon Knowledge Engineering Tools and Techniques for AIPlanning Toulouse, France 24 April 2002

Alexander Nareyek, Eugene C. Freuder, Robert Fourer, EnricoGiunchiglia, Robert P. Goldman, Henry Kautz, Jussi Rintanen,Austin Tate. ”Constraints and AI Planning,” IEEE IntelligentSystems, vol. 20, no. 2, pp. 62-72, March/April, 2005.

Biplav Srivastava and Jana Koehler : Planning withWorkflows - An Emerging Paradigm for Web ServiceComposition, ECOOP December 2004.


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References and Links Contd ...

J. Koehler, J. Hoffmann: “On Reasonable and Forced GoalOrderings and their Use in an Agenda-Driven PlanningAlgorithm”, Journal of Artificial Intelligence Research,12/2000, pages 339-386.

B. Nebel, Y. Dimopoulos, J. Koehler,“ Ignoring IrrelevantFacts and Operators in Plan Generation”, ECP-97, pages338-350

“The Role of Planning in Grid Computing” Jim Blythe, EwaDeelman, Yolanda Gil, Carl Kesselman, Amit Agarwal,Gaurang Mehta and Karan Vahi , International Conference onAutomated Planning and Scheduling, ICAPS 2003


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Finding plans and defining plan constraints simultaneously”Jim Blythe KCAP 01 Workshop on Interactive Tools forKnowledge Capture, 2001

Thomas A. Montgomery and Edmund H. Durfee, “SearchReduction in Hierarchical Distributed Problem Solving”.Group Decision and Negotiation 2:301-317 (Special issue onDistributed Artificial Intelligence), 1993.

http://www.cs.cmu.edu/ reids/planning/notes.html ( Date :10 / 10 / 2005 )


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http://www.laas.fr/planning/ ( date : 4/11/2005)

http://www.planet-noe.org (Date: 23/10/2005 )

http://www.informatik.uni-freiburg.de/ ki/lehre/ss04/aip/(Date 10/10/2005)

Book: Artificial Intelligence A modern approach”, Russel. Sand Norvig, Pearson Education Publishers, 2003.


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Thank You


application of artificial intelligent planning · ai planning is ubiquitous in on a daily basis...

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