1. Consiglio Nazionale delle Ricerche
Istituto di Calcolo e Reti ad Alte Prestazioni
Towards Self-Adaptation and
Evolution in Business Process
Luca Sabatucci
15 Ottobre 2013

3. BPMN
Commercial workflow engines follow the
plan as in the blueprint (BPMN, BPEL)
The execution model is based on the
petri-net model: token and activation
•

4. Self-Adaptive Workflows
•
In current workflow systems,
• a unexpected situation generates a fail to the
standard plan,
• alternative plans can not be autonomously
considered
•
In order to enable self-adaptation,
• rigid constraints of a workflow must be relaxed
• plans should be searched in a broader solution
space.

5. Our Approach

6. FROM BPMN to Goals

7. Decoupling WHAT and HOW
We
desire that BPMN is used for
◦ describing the result to address,
◦ not “how to address it”
We
use a Goal-Oriented approach
GoalSPEC: a language to describe
business goals to delegate to the system

8. Goal in GoalSpec
Goal
TRIGGERING CONDITION
The state of the world that
must hold because the goal
becomes active
ACTOR LIST
Who is responsible
FINAL STATE
The state of the world that
must be true for considering
the goal addressed
In oorderto des
In rder to des ibe
cr
cr
Trigggercco dit ibe
Tri ger onndit n
io n
io
andd
an
FFinalstaate
inal st te
weeuusean oo to
w se an nnto gica
lo gicalapppro h
lo l ap roac h
ac
(WHEN MESSAGE book_request(Book)
RECEIVED FROM THE Client ROLE
AND WHEN available(Book) )
THE Clerk ROLE SHALL ADDRESS
book_checkout(Book,Client)
(BookManagment example)
Each goal in GoalSPEC
Each goal in GoalSPEC
defines aa
defines
ition,
desireddStateeTrans ition,
desire Stat Trans
G:TC -> FS
G:TC -> FS

9. Extracting GoalSPEC from BPMN
The workflow execution may be seen as a
finite set of state transitions from the
start event to the end event.
Where: each FlowNode generates a
single step of the transition
We identify intermediate transitions

10. Examples
Every FlowNode potentially impacts the state
of the workflow
WHEN completed(client_credential)
THE user role SHALL ADDRESS
( done(booking) AND sent(receipt,client) )
OR error(booking)
AFTER 1 hour SINCE WHEN done(login)
OR WHEN user_number > MAX_USERS
OR WHEN thrown(stopping_signal)
THE user role SHALL ADRESS
done (logout)

11. THE Agent ARCHITECTURE

12. Why Software Agents
Agents encapsulate autonomy and
proactivity.
Agents ground on the classic AI loopmodel: sense – reason – act
BDI agents represent a good abstraction
for self-awareness and contextawareness
MAS are a flexible and powerful method
for distributed reasoning

13. The BDI Architecture
(mental states)
Agent1
Agent2
AgentN
(plans)
(act and perceive)
Environment
13

14. Context-Awareness and SelfAwareness
•
Proactive contextual matching of the
behavior (how) with expected results (what)
•
The agent must be able to reason
• on evolving business goals
• on the execution context
• on its capabilities

15. The Self-Aware Agent
SYSTEM GOALS
Self-Aware agents
knows:
•system goals
•their own execution
state
•their capabilities and
how capabilities can be
used
(means-end
reasoning)
CAPABILITIES
Agent1
Agent2
AgentN
(plans)
(UI)
Capability encapsulat
es
the ability to manipulat
e
resources, to call web
services or to interact
with humans
Environment
System goals
derive from business
analysis, when some
business goal is
delegated to the
system
t)
en
tm
mi
m
(co
(UI)
Resources
(databases, …)
Business
Analyst
Web Service
Web Service
Workflow
User
15

16. Self-Organization Algorithm
Goal commitment is a social activity
Each goal prescribes a state transition (TC ->
FS)
Agents may own capabilities for addressing
sub-transitions (sinput -> soutput)
A Solution is a decomposition of the main
transition TC -> FS into a set of sub-transitions
A Team prescribes a collaboration among many
agents, and it is regulated by contracts and
rewards.

17. A solution is a set of potential contracts for addressing sub-goals:
contract( potential commitment, curriculum, request income)
TThealalgori m
he gorith
thmfor
distriribute an forsearching gsolutitions is
dist butedd an re searchin solu ons is
dd recursiv
cursivee
system-goal
goal-set
{TC | FS}
sub-goal
G1..G5
G1
G2
{TC | IS1}
AGENTS
G3
{IS1 | IS2}
{IS2 | IS3}
G5
{IS3 | IS4}
I may
commit
to G3
A1
ch
Agents tries sto mat ch
Agents trie to mat goal
s
their rcapacicitieswith goal
thei capa tie with
transitionnTC -> FS
transitio TC -> FS
de IF
Agents alalsodecicide IF
Agents so de
the
PARTICIPATEEto the
PARTICIPAT to H
solutionnanddWHIC H
solutio an WHIC
PART play in it,
PART play in it,
r
depending gon thei r
dependin on thei
workinngqueuee
worki g queu
G4
AN
Aj
Recursionnstops w
Recursio stops w n no
he
he
solutionnisisdiscover n no
solutio
discoveeddor in
re or in
theetrivivialcase of nu
th tr ial case of nu
ll ll
transition
transition
contract( {IS2 | IS3}, my_curriculum, 1)
backward-goal
decompose and recursively
search sub-goals
{TS | IS2}
A1
A2
{IS4 | FS}
forward-goal
{IS3 | FS}
AN
A1
A2
AN
ay be
Many ysolutions m ay be
Man solutions m
discovered. .
discovered
alu ed
ev ated
Each solutionnisisev aluat
Each solutio
according gto: :
accordin to
ts’
completeness,s,agen ts’ )
completenes agen cost )
tal
st
reputationnanddto tal co
reputatio an to

18. Conclusions:
The new Lifecycle of Business Process
Business
Analyst
models
Theebusiness sanalyst
Th busines analyst itional
l
continues sto useetrad itiona
continue to us tradhis
odel his
instruments to m odel
instruments to m
processes s
processe
revises
The system
automatically
translates BPMN
into goals
Every yagent in th
Ever agent in th syst
ee em
autonomously de system
autonomously de des
ci
whennto commit cides
whe to committo so
me
to some
goalal
go
GoalSPEC isis a
GoalSPEC a
language for
language for
expressing ggoals
expressin goals
that tgrounds son
tha ground on
ontology yandd
ontolog an
defines sa agoalalas the
define go as the
tuple
tuple
(actor,r,
(acto
trigger-condition,
trigger-condition,
final-state) )
final-state
GoalSPEC
injection
worker
Business
Expert
interacts
commits to
analyzes
analyzes
Running MAS
analyzes
commits to
analyzes
18
Environment perturbations

19. Conclusions:
The components of the MAS Solution
•
Agents are specialized: every agent owns its own capacities.
– User capacities are used to interact with humans and monitor their activity
– Service capacities are used to manipulate the environment.
•
Agent are peers: there is not a pre-established organization. They
organize themselves in teams every time a new workflow starts
– The self-org algorithm considers many criteria (experience, cost, trust)
– The team is the candidate group of agents for addressing the workflow, in a
given context
– Anyway, the context may change for some reason during the execution:
• In case of starvation the team tries to relax some constraints
• If some task fails, the team is dismissed and a new team is formed
• If no alternative team can be found, the analyst is informed of failure
•
Commitment: when an agent is involved in a team, it tries to address
its responsibilities at best of its possibilities
– It waits for triggering conditions hold
– It selects and executes the proper capacity or composition of capacities
– It checks that result is the expected final state
•
Trust and Reward:: agents that successfully complete their task gain
reputation and they increase their chance to be selected again.

20. Future Works
Agents
Learns by
◦ Experience  to improve owned capabilities
◦ Studying  to acquire new capabilities
Coupling
Goals and Norms in GoalSPEC
Open Systems and Clouds

21. Thanks for your Attention
Luca Sabatucci
sabatucci@pa.icar.cnr.it
Consiglio Nazionale delle Ricerche
Istituto di Calcolo e Reti ad Alte Prestazioni