Towards Self-Adaptation Towards Self-Adaptation and Evolution in Business and Evolution in Business

ProcessProcess

Luca Sabatucci

15 Ottobre 2013

Consiglio Nazionale delle Ricerche

Istituto di Calcolo e Reti ad Alte Prestazioni

BPMNBPMN• 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

Self-Adaptive WorkflowsSelf-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.

Our ApproachOur Approach

FROM BPMN to GoalsFROM BPMN to Goals

Decoupling WHAT and Decoupling WHAT and HOWHOWWe desire that BPMN is used for

◦describing the result to address, ◦not “how to address it”

We use a Goal-Oriented approachGoalSPEC: a language to

describe business goals to delegate to the system

Goal in GoalSpecGoal in GoalSpec

Goal

TRIGGERING CONDITIONThe state of the world that must hold because the goal

becomes active

ACTOR LISTWho is responsible

FINAL STATEThe state of the world that

must be true for considering the goal addressed

(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)

In order to describe Trigger condition and Final state we use an ontological approach

In order to describe Trigger condition and Final state we use an ontological approach

Each goal in GoalSPEC

defines a

desired State

Transition,

G:TC -> FS

Each goal in GoalSPEC

defines a

desired State

Transition,

G:TC -> FS

Extracting GoalSPEC from BPMNExtracting GoalSPEC from BPMNThe 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

ExamplesExamplesEvery 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_USERSOR WHEN thrown(stopping_signal)THE user role SHALL ADRESSdone (logout)

THE Agent ARCHITECTURETHE Agent ARCHITECTURE

Why Software AgentsWhy Software AgentsAgents encapsulate autonomy and

proactivity.Agents ground on the classic AI loop-

model: sense – reason – actBDI agents represent a good abstraction

for self-awareness and context-awareness

MAS are a flexible and powerful method for distributed reasoning

The BDI ArchitectureThe BDI Architecture

13

Environment

Agent1 Agent2 AgentN(plans)

(mental states)

(act and perceive)

Context-Awareness and Self-Context-Awareness and Self-AwarenessAwareness

• 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

The Self-Aware AgentThe Self-Aware Agent

15

Resources(databases, …) Web Service

Environment Web Service

SYSTEM GOALS

Business Analyst

Workflow User

(UI)

(UI)

Agent1 Agent2 AgentN

System goals derive from business analysis, when some business goal is delegated to the system

Self-Aware agents knows:•system goals•their own execution state •their capabilities and how capabilities can be used

Capability encapsulates the ability to manipulate resources, to call web services or to interact with humans

CAPABILITIES (plans)

(commitment)

(means-endreasoning)

Self-Organization AlgorithmSelf-Organization AlgorithmGoal 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.

A solution is a set of potential contracts for addressing sub-goals:

contract( potential commitment, curriculum, request income)The algorithm for searching solutions is distributed and recursive

The algorithm for searching solutions is distributed and recursive

Agents tries to

match their

capacities with goal

transition TC -> FS

Agents also decide

IF PARTICIPATE to

the solution and

WHICH PART play in

it, depending on

their working queue

Agents tries to

match their

capacities with goal

transition TC -> FS

Agents also decide

IF PARTICIPATE to

the solution and

WHICH PART play in

it, depending on

their working queue

Recursion stops when no solution is discovered or in the trivial case of null transition

Recursion stops when no solution is discovered or in the trivial case of null transition

Many solutions may

be discovered.

Each solution is

evaluated according

to: completeness,

agents’ reputation

and total cost)

Many solutions may

be discovered.

Each solution is

evaluated according

to: completeness,

agents’ reputation

and total cost)

Conclusions: Conclusions: The new Lifecycle of Business ProcessThe new Lifecycle of Business Process

18Running MAS

GoalSPEC

Business Analyst

Business Expert

models revises

The systemautomatically translates BPMN into goals

injection

analyzes

analyzes

commits to

commits to

analyzesanalyzes

worker

interacts

The business analyst

continues to use

traditional

instruments to model

his processes

The business analyst

continues to use

traditional

instruments to model

his processes

GoalSPEC is a

language for

expressing

goals that grounds on

ontology and

defines a goal

as the tuple(actor,trigger-condition,final-state)

GoalSPEC is a

language for

expressing

goals that grounds on

ontology and

defines a goal

as the tuple(actor,trigger-condition,final-state)

Every agent in the system autonomously decides when to commit to some goal

Every agent in the system autonomously decides when to commit to some goal

Environment perturbations

Conclusions:Conclusions:The components of the MAS SolutionThe 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.

Future WorksFuture WorksAgents Learns by

◦Experience to improve owned capabilities

◦Studying to acquire new capabilities

Coupling Goals and Norms in GoalSPEC

Open Systems and Clouds

Thanks for your AttentionThanks for your Attention

Luca Sabatuccisabatucci@pa.icar.cnr.it

Consiglio Nazionale delle Ricerche

Istituto di Calcolo e Reti ad Alte Prestazioni