s-cube lp: ai planning based composition of pervasive process fragments

www.s-cube-network.eu

S-Cube Learning Package

Automated Service Composition:

AI planning based composition of pervasive process fragments

Fondazione Bruno Kessler (FBK),

University of Stuttgart (USTUTT)

Annapaola Marconi, FBK

Learning Package Categorization

S-Cube

Adaptable Coordinated

Service Compositions

Automated Service Composition

AI planning based composition of

pervasive process fragments

Learning Package Overview

Problem Description

Application Representation

Automated composition of process-fragments

Evaluation and discussion

Related Works

Conclusions

Pervasive applications require processes to be discovered and used

depending on the context (location, time, situation, user preferences)

Problem Description

Pervasive process

fragments allow to model

incomplete and contextual

process knowledge

Automated composition

allow to synthesize a

process on-the-fly given a

set of components

Automated composition of pervasive process fragments

into a complete executable process, according to a goal

and a specific context

Unload from airplane Check box content

Charge and collect tax

Take to baggage claim

Box at the Airport Flow

Goal: Release box

Check box origin

Scenario: Box at the airport (1)

Unload from airplane Check box content



Box at the Airport Flow

Goal: Release box

Check box origin


The process knowledge for

handling a box is distributed at

different locations in the airport

and depends on context information.

The complete executable flow model for treating a particular box is not

known from the beginning.

What is known is the goal of the flow, release the box to its owner, and

the steps/milestones that the box should go through to achieve its goal,

e.g. unload from airplane, check box origin, charge and collect tax

The precise flow model that can achieve this goal is created at

execution time, based on the available fragments and on the specific

context (e.g. content of the box, box origin, arriving airport )

Goals

Primary: Box.released

Recovery: Box.disposed

Do

mai

n

Kn

ow

led

ge

Box


Domain knowledge represent the stable and abstract common

knowledge of the context and the of the processes for a specific

pervasive application.

Object diagrams model the macro steps/milestones that a process

should go through

Goals represent the target of the process execution and can contain

preferences among goals and recovery goals

Object Diagrams

Pervasive Process Fragments

Fra

gm

ent

modelin

g

Goals Object Diagrams



Do

mai

n

Kn

ow

led

ge

Box



represent the dynamic and

concrete process knowledge:

They are concrete processes

discovered at run time and

targeted to a specific context

Where?

What?

Who?

?

Box Delivery Process


Pro

ce

ss e

xe

cu

tio

n

Fra

gm

ent

modelin

g

Goals

Context

Object Diagrams



Verona International Airport

Flammable content

DHL

Do

mai

n

Kn

ow

led

ge

Box


Where?

What?

Who?

Box Delivery Process


Pro

ce

ss e

xe

cu

tio

n

Fra

gm

ent

modelin

g

Goals

Context

Object Diagrams



Verona International Airport

Flammable content

DHL

Co

mm

on

K

no

wle

dge

Box


Fragment

selection

? ?


Problem Description




Related Works

Conclusions


Goals

Stable and

abstract

Pervasive process

fragments annotated with

preconditions and effects

(domain knowledge)

P: … P: …

E: …

E: …

Pro

ce

ss

kn

ow

led

ge

Dynamic and

concrete

Do

ma

in

kn

ow

led

ge

Object diagrams

Object diagrams

Goals

Pervasive process



(domain knowledge)

P: …

P: …

E: …

E: …

Object diagrams

An object diagram is a simple state transition system

containing states which encode properties of the

entity, and transitions between states triggered by

events.

Research work on object diagrams:

Piergiorgio Bertoli, Raman Kazhamiakin, Massimo Paolucci, Marco Pistore, Heorhi Raik, Matthias

Wagner: Control Flow Requirements for Automated Service Composition. ICWS 2009: 17-24

Raman Kazhamiakin, Massimo Paolucci, Marco Pistore, Heorhi Raik: Modelling and Automated

Composition of User-Centric Services. OTM Conferences (1) 2010: 291-308

Object diagrams (2)

box at the

airport tax

invoice

Object diagrams in the Box at the airport scenario

The diagrams move from one configuration to another on events.

For example, if the box is in configuration INIT and receives the event unload, it will

move to configuration UNLOADED

Goals

Goals

Pervasive process



(domain knowledge)

P: …

P: …

E: …

E: …

Object diagrams

We express goals in terms of entities and their evolution.

Goals can be used to specify desirable situations to be

reached at the end of the execution, as well as rules that

should be maintained throughout the execution.

Research work on control flow goals:

Piergiorgio Bertoli, Raman Kazhamiakin, Massimo Paolucci, Marco Pistore, Heorhi Raik, Matthias

Wagner: Control Flow Requirements for Automated Service Composition. ICWS 2009: 17-24

A goal is defined with the following generic constraint template:

where

ss(o) defines the fact that diagram o is in configuration s, and ee(o) the fact that event e of o has taken place.

Goals (2)

Composition goal

T => readys(box) ≻ disposeds(box)

box at the

airport tax

invoice

Primary Goal Recovery Goal

Control flow goal for the Box at the airport scenario

The goal for the box is to reach the configuration READY. If this is not possible, we

at least want to have the box disposed of, therefore in configuration DISPOSED.

Process fragments represent a tool for modeling

incomplete and local process knowledge.

The knowledge is incomplete since the modeler is

allowed to specify just one aspect of the entire

process, and to leave gaps in the process specification.

Fragments can be modeled by different people, and therefore may

reflect different perspectives on the same process.

The process knowledge is local, since the availability and usability of a

fragment is determined by the context. For example, the execution of a

process fragment may be bound to a certain location or to a specific

context property.

Process fragment knowledge can be integrated dynamically, either at

design-time or at run-time.

Processes are enriched with goals which specify what is pursued by the

process execution. It also requires enriching the fragments with

information on how they contribute to the outcome of the process.


Goals

Pervasive process



(domain knowledge)

P: …

P: …

E: …

E: …

Object diagrams

Pervasive process fragments

-> incomplete contextual process knowledge:

Specified in APFL = BPEL + extensions for pervasive domain

Not required to have a start activity

Control connectors may have either no source or no target

activity

Modeler has freedom to not model control connectors at all

Can contain gaps -> Region element

Research work on pervasive process fragments:

Pervasive Process Fragments (2)

H. Eberle, T. Unger, and F. Leymann, “Process fragments” in OTM 2009,

Part I, pp. 398–405.

A. Bucchiarone, A. L. Lafuente, A. Marconi, and M. Pistore, “A formalisation

of adaptable pervasive flows” in 6th Int. Workshop on Web Services and

Formal Methods (WS-FM), 2009

A. Marconi, M. Pistore, A. Sirbu, F. Leymann, H. Eberle, and T. Unger,

“Enabling Adaptation of Pervasive Flows: Built-in Contextual Adaptation” in

Proc. ICSOC 2009, pp. 445–454.

(some) APFL activities

receive

reply,

one-way invoke

two-way invoke

human

interaction

context event

event-based

exclusive

decision (pick,

apfPick)

region

Check EU origin

Receive EU origin ack

Charge tax

Receive EU origin nack

Box at baggage claim

Check box origin

Check box content

Bring to claim

Unload box

Box on carrier

Box at termina

l

Content ack

Mark box

Check content

Dispose

Unload from airplane

Bring to terminal


P: unloadeds(box)

P: unloadeds(box) E: evaluatee(box)

P: taxeds(box) E: approvee(box)

P: unloadeds(box) E: approvee(box)

P: unloadeds(box) E: rejecte(box)

P: rejecteds(box) E: disposee(box)

P: approveds(box) E: releasee(box)

P: inits(box) P: inits(box) E: unloade box)

P: unloadeds(box)

Content nack

P: approveds(box)


Charge tax with invoice

P: unloadeds(box) ʌ not-exists(inv) E: {evaluatee(box), createe(inv)}

Send notice of assessment

P: opens(inv)

Payment completed

P: evaluateds(box) ʌ opens(inv) E: {taxe(box), closee(inv)}

Pervasive process fragments (3)

Check box content

Unload box


Bring to claim

Check box origin

Check EU origin


Charge tax


Box at baggage claim

Check box origin

Check box content

Bring to claim

Unload box

Box on carrier

Box at termina

l

Content ack

Mark box

Check content

Dispose

Unload from airplane

Bring to terminal


P: unloadeds(box)

P: unloadeds(box) E: evaluatee(box)

P: taxeds(box) E: approvee(box)


P: unloadeds(box) E: rejecte(box)

P: rejecteds(box) E: disposee(box)

P: approveds(box) E: releasee(box)

P: inits(box) P: inits(box) E: unloade box)

P: unloadeds(box)

Content nack

P: approveds(box)


Charge tax with invoice

P: unloadeds(box) ʌ not-exists(inv) E: {evaluatee(box), createe(inv)}

Send notice of assessment

P: opens(inv)

Payment completed

P: evaluateds(box) ʌ opens(inv) E: {taxe(box), closee(inv)}

Pervasive process fragments (4)

box at the

airport

Activities of pervasive process

fragments can be annotated with

Preconditions: constraints on the

context where the activity can be

executed

Effects: changes that the activity

execution produces on the

context


Problem Description




Related Works

Conclusions

Object diagrams

Pervasive

process

fragments

Composed

executable

flow

Goals

Automated

Composition Box

T => readys(box) ≻

disposeds(box)

Solution overview

Object diagrams

Pervasive

process

fragments

Composed

flow

Goals

Box


disposeds(box)

OD

-2-S

TS

STS

STS

(2)

G-2

-ST

S

STS

(3)

Acti

on

Tab

le

AP

FL

-2-S

TS

(1)

Solution overview

GO

AL

Co

ns

tru

cti

on

ρ

Planning

goal

Object diagrams

Pervasive

process

fragments

Goals

Box


disposeds(box)

OD

-2-S

TS

STS

STS

(2)

G-2

-ST

S

STS

(3)

Acti

on

Tab

le

AP

FL

-2-S

TS

(1)

Solution overview

GO

AL

Co

ns

tru

cti

on

ρ

Planning

goal

Background

A state transition system (STS) is a tuple

<S, S0, I, O, R, Sf, F>

where S is the set of states and S0 is the set of initial states,

I and O are the input and respectively output actions,

is the transition relation,

SF is the set of accepting states,

is the labeling function.

Composed

flow

Solution (1) pervasive process fragments to STS

Check EU

origin



Check box origin

P: unloadeds(box)


<unloads(box), !Receive_EU_origin_ack, {approvee(box)}>

STS

Action table entry

With this translation, we lose the information encoded in the effects of

activities.

We capture this information with a second data structure (action table entry), which

will be used when transforming the object diagrams and the goals

New STS:

- no events

- all states are final

- labels for states

Solution (2) object diagrams to STS

Composition goal

T => readys(box) ≻ disposeds(box)

!eT !er

[readys(box) ]

!ed

[disposeds(box)]

?eT

?eT ?eT

?er ?ed

l0

l1 l2

ρ = (l0, l1, l2)

Solution (3) goals to STS

STSs

For each goal, we construct the STSs

that correspond to the satisfiability of the

goal.

For every formula we define a single

output action e which gets triggered when

the formula is satisfied.

We use these completion actions for

composing the formulas.

The preconditions on the activities will be

carried over as guards also in the goal

STSs ρ

Solution overview

Object diagrams

Pervasive

process

fragments

Composed

flow

Goals

Box


disposeds(box)

OD

-2-S

TS

STS

STS

(2)

G-2

-ST

S

STS

(3)

Acti

on

Tab

le

AP

FL

-2-S

TS

(1)

Planning

domain

STS

(4)

GO

AL

Co

ns

tru

cti

o

n ρ

Planning

goal

Pla

nn

er

(5)

Controlled

domain

ST

S-2

-AP

FL

(6)

Solution (4) building the planning domain

The planning domain is the parallel product of the STSs resulting from

the transformation of fragment models, object diagrams, and goals,

capturing their simultaneous evolution.

Background

Let = and = be two STSs with

.

The parallel product is a STS defined as:

Where

and

Solution (5) obtaining the composed STS

We exploit the ASTRO automated composition approach - www.astroproject.org

Sophisticated AI planning techniques (Planning as Model Checking)

Asynchronous domains, non-determinism, partial observability

Complex goals: preferences and recovery conditions (EaGle)

Control and data flow composition requirements

Research work on ASTRO automated service composition:

Annapaola Marconi, Marco Pistore: Synthesis and Composition of Web Services. SFM 2009: 89-157

Annapaola Marconi, Marco Pistore, Paolo Traverso: Automated Composition of Web Services: the

ASTRO Approach. IEEE Data Eng. Bull. 31(3): 23-26 (2008)

Annapaola Marconi, Marco Pistore, Piero Poccianti, Paolo Traverso: AutomatedWeb Service

Composition at Work: the Amazon/MPS Case Study. ICWS 2007: 767-774.

Annapaola Marconi, Marco Pistore, Paolo Traverso: Specifying Data-Flow Requirements for the

Automated Composition of Web Services. SEFM 2006: 147-156

M. Pistore, P. Traverso, P. Bertoli, and A. Marconi, Automated synthesis of composite BPEL4WS web

services” in Proc. ICWS 2005

http://www.astroproject.org/

Un

load

b

ox

Check box content

Bring to claim

Solution (5) obtaining the composed STS

Bring to claim

Ch

arge

an

d c

olle

ct t

ax

Composed STS for the Box at

the airport scenario

Solution (7) result as APFL

The translation from STS to APFL is conceptually simple, and is

performed based on action names.

From the construction of STSs, each action name is unique and

corresponds to at most one appearance of an activity in a fragment model

Such actions can therefore be mapped back to their corresponding

activities


Problem Description




Related Works

Conclusions

We implemented our approach into a prototype tool.

The tool translates the input object diagrams (XML), and the fragment models and

goals (APFL) into a planning problem.

For the planning problem we use a customized NuSMV language, extended to

allow the specification of goals with preferences.

The planning problem is then passed to WSYNTH, one of the tools in the ASTRO

toolset.

The output returned by WSYNTH is the controlled domain which we then translate

back to APFL.

To evaluate our tool, we consider several features specific to fragment

composition.

First, the set of available fragment models can contain more models then actually

necessary for composition.

Second, there is a tradeoff between designing fragment models with a large

number of activities (a higher burden on the designer) and with a small number

(longer composition time).

Finally, fragment models can include overlapping activities.

Evaluation

Evaluation

Tradeoff regarding the number of activities in a fragment:

large => higher burden on the designer

small => longer composition time

Discussion Comparison to Web Service Composition

Research problem:

Compose pervasive process fragments at run time based on context and

goals

Comparison to Web service composition:

Components are not orchestrated, but integrated into a complete process

model

New problems: fragments can overlap, fragments can have gaps in the

specification

Context plays a key role both in terms of modeling the contextual

information and take contextual constraints into account during the

composition

Further Readings

A. Marconi, M. Pistore, A. Sirbu, F. Leymann, H. Eberle, and T. Unger, Dynamic Composition of Pervasive Process

Fragments in Proc. ICWS 2011.

R. Kazhamiakin, M. Paolucci, M. Pistore, H. Raik: Modelling and Automated Composition of User-Centric Services.

OTM Conferences (1) 2010: 291-308

A. Marconi, M. Pistore: Synthesis and Composition of Web Services. SFM 2009: 89-157

P. Bertoli, R. Kazhamiakin, M. Paolucci, M. Pistore, H. Raik, M. Wagner: Control Flow Requirements for Automated

Service Composition. ICWS 2009: 17-24

P. Bertoli, R. Kazhamiakin, M. Paolucci, M. Pistore, H. Raik, M. Wagner: Continuous Orchestration of Web Services via

Planning. ICAPS 2009

http://portal.acm.org/citation.cfm?id=1719370

Acknowledgements

The research leading to these results has

received funding from the European

Community’s Seventh Framework

Programme [FP7/2007-2013] under grant

agreement 215483 (S-Cube).

s-cube lp: ai planning based composition of pervasive process fragments

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