chapter 15: agents service-oriented computing: semantics, processes, agents – munindar p. singh...

Chapter 15:Agents

Service-Oriented Computing: Semantics, Processes, Agents– Munindar P. Singh and Michael N. Huhns, Wiley, 2005

Chapter 15 2Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and

Michael Huhns

Highlights of this Chapter

Agents Introduced Agent Descriptions Abstractions for Composition Describing Compositions Service Composition as Planning Rules


Michael Huhns

What is an Agent?

Wide range of behavior and functionality in computing

An agent is an active computational entity With a persistent identity

Can carry out a long-lived conversation Perceives, reasons about, and initiates

activities in its environment Deals with services

Communicates (with other agents) and changes its behavior based on others

Loosely coupled Business partners map to agents


Michael Huhns

Agents and Multiagent Systems for SOC

Unlike objects, agents Are proactive and autonomous Support loose coupling

In addition, agents may Cooperate or compete Model users, themselves, and others Dynamically use and reconcile

ontologies


Michael Huhns

Modeling Agents: AI

Emphasize mental concepts Beliefs: agent’s representation of the

world Knowledge: (usually) true beliefs Desires: preferred states of the world Goals: consistent desires Intentions: goals adopted for action

Resources allocated Sometimes associated with an element of

persistence


Michael Huhns

Modeling Agents: MAS

Emphasize interaction Social: about collections of agents Organizational: about teams and

groups Legal: about contracts and

compliance Ethical: about right and wrong actions

Emphasize autonomy and communication


Michael Huhns

Mapping SOC to Agents

Agents apply well in an open system Autonomy ability to enter into and

enact contracts; compliance How can we check or enforce

compliance? Heterogeneity ontologies Loose coupling communication Trustworthiness contracts, ethics,

learning, incentives Dynamism combination of the

above


Michael Huhns

A Reactive AgentThe Sense-Decide-Act Loop

PerceiveEnvironment

Select Action

Environment

Condition-Action Rules

Effectors

Sensors

percepts

action

worldmodel

outputs

inputs

ReactiveAgent

Environment e;RuleSet r;while (true) { state = senseEnvironment(e); a = chooseAction(state, r); e.applyAction(a);}


Michael Huhns

Economic Rationality

Three elements A performance measure, e.g., expected

utility An agent’s prior knowledge and perceptions The available actions

Ideal: for each possible percept sequence, Acts to maximize its expected utility On the basis of its knowledge and evidence

from the percept sequence


Michael Huhns

Logic-Based Agents(Another form of rationality)

An agent is a knowledge-based system Represents a symbolic model of the

world Declarative (hence, inspectable) Reasons symbolically via logical

deduction Challenges:

Representing information symbolically Easier in information environments than

in general Maintaining adequate model of the

world


Michael Huhns

Cognitive Architecture for an Agent

Beliefs, Desires, Intentions

Reasoner

Effectors

Sensors

Perceptions

Actions

Agent Alice

Beliefs, Desires, Intentions

Reasoner

Effectors

Sensors

Perceptions

Actions

Agent Bob

CommunicationInfrastructure

CommunicationInterfaces

For SOC, sensors and effectors map to services; the communication infrastructure is messaging middleware

Exercise

Create an instance of the preceding diagram where the two agents are Amazon and a manufacturer When is it beneficial to employ agents in

this setting? What is an illustration of loose coupling in

this setting?


Michael Huhns


Michael Huhns

Action

output

brf

Generate options

filter

action

Sensor

input

beliefs

desires

intentions

Generic BDI Architecture

• Addresses how beliefs, desires and intentions are represented, updated, and acted upon

• Somewhat richer than sense-decide-act: decisions directly affect future decisions

• Consider goal-oriented requirements engineering


Michael Huhns

Architecture of BDI-Based AgentExecution Cycle: the agent1. Receive new

information2. Update beliefs and

goals3. Reason about actions4. Intend an action5. Select an intended

action 6. Activate selected

intention7. Perform an action8. Update beliefs, goals,

intentions

+run()+currentIntentionIsOK() : boolean(idl)+stopCurrentIntention()+chooseIntention()+perceiveEnvironment()+takeAction()

-B : BeliefSet-D : DesireSet-P : IntentionSet-I : Intention-e : Environment-name: String-a : Action

Agent

+run()+applyAction(in a : Action)

-a : AgentSet

Environment

+add(in a : Agent)+remove(in a : Agent)

-elements: Vector

AgentSet

+includeObservation()

-elements: Vector

BeliefSet

+getApplicable(in B : BeliefSet) : DesireSet

-elements: Vector

DesireSet

+getApplicable(in D : DesireSet, in B : BeliefSet) : IntentionSet

-elements: Vector

IntentionSet

+satisfies(in d : Desire) : boolean(idl)+execute(in a : Agent) : boolean(idl)+context(in B : BeliefSet) : boolean(idl)+stopExecuting()

-id: String-priority: int-d: Desire-a : Agent

Intention

-id: String-value: String

Belief

+context(in B : BeliefSet) : boolean(idl)

-id: String-priority: int

Desire

Action


Michael Huhns

Web Ontology Language for Services (OWL-S)

An OWL-S service description provides Declarative ads for properties and

capabilities Used for discovery

Declarative APIs Used for execution

A declarative description via inputs, outputs, preconditions, effects (IOPE) Used for composition and interoperation Extended to IOPR: a result combines an

output and associated effects


Michael Huhns

OWL-S Service Ontology

Service

ServiceGrounding

Resource

ServiceModel

ServiceProfile

provides

supports presents

describedBy


Michael Huhns

OWL-S Mapped to UDDI


Michael Huhns

OWL-S Service Model

Resource Service

ServiceProfile ServiceGrounding

ProfileProcess

AtomicProcess SimpleProcess CompositeProcess

ControlConstruct

ServiceModel

ProcessComponent

input

precondition

output

effect

provides

presents describedBy supports

hasProfile

realizes expand

components

computedInput

computedEffectinvocable

computedOutput

composedBy

computedPrecondition

Sequence Split RepeatUnit. . .

QualityRating

ServiceCategoryActor

ParameterDescription

ServiceParameter


Michael Huhns

OWL-S Example: Processing Book Orders

CreateAccount

LoadAccount

ChooseBook

Add toOrder

SelectCredit Card

ChargeCredit Card

Book StoreSequence Process

Selection Process Iteration Process Choice Process

Choice Process


Michael Huhns

OWL-S IOPEs for Bookstore Example


Michael Huhns

Composition as Planning

Represent current and goal states Represent each service as an

action Based on its IOPE

Represent a composed service as a plan that invokes the constituent services constraining the control and data flow to achieve the goal state


Michael Huhns

Rules: Logical Representations

Rules are desirable because they are Modular: easy to read and maintain Inspectable: easy to understand Executable: no further translation

needed Expressive: (commonly) Turing

complete and can capture knowledge that would otherwise not be captured declaratively

Compare with relational calculus (classical SQL) or description logics (OWL)

Declarative, although imperfectly so


Michael Huhns

Kinds of Rules

ECA or Reaction On event if condition then perform action

Derivation rules: special case of above Integrity constraints: derive false if

error Inference rules

If antecedent then consequent Support multiple computational

strategies Forward chaining; backward chaining


Michael Huhns

Applying ECA Rules

Capture protocols, policies, and heuristics as ECA rules Examples?

Often, combine ECA with inference rules (to check if a condition holds)

Modeling challenge What is an event? How to capture composite events by

pushing event detection to lower layers

Example: ECA

IF request (?x ?y ?z) event

AND like (?x ?y) condition

THEN do(fulfill(?x ?z)) action

1. Watch out for relevant events

2. If one occurs, check condition

3. If condition holds, perform action


Michael Huhns

Example: Inference

Typical syntax indicating forward chaining

IF parent(?x ?y)

AND parent (?y ?z) Antecedent

THEN grandparent (?x ?z) Consequent

Typical syntax indicating backward chaining

INFER grandparent (?x ?z) Consequent

FROM parent(?x ?y) Antecedent

AND parent (?y ?z)


Michael Huhns

Example: Communication

IF incoming-message(?x ?y ?z)

AND policy(?x ?y ?w)

AND policy(?x ?z ?v)

THEN send message(?x ?v ?w)

AND assert internal-fact(?x ?v ?w) Here the policy stands for any internal

decision making, usually defined as INFER policy(?x ?y ?w) FROM …


Michael Huhns

Exercise

State the customer’s rules to capture how it might interact with a merchant in a purchase protocol RFQ: request for quotes (Price) quote Accept or Reject Goods Payment Receipt


Michael Huhns


Michael Huhns

Applying Inference Rules

Capture general requirements Elaboration tolerance requires

defeasibility Conclusions are not firm in the face of new

information Formulate general rules Override rules to specialize them as needed

Leads to logical nonmonotonicity Easy enough operationally but difficult to

characterize mathematically Details get into logic programming with

negation


Michael Huhns

Free and Bound Variables

General rules involve free variables For ECA rules: in event and condition

Free variable in action indicates perform action for each binding

For inference rules: in antecedent Free variable in consequent means assert

it for each binding

Therefore, to ensure safety, use only bound variables in action or consequent


Michael Huhns

Chapter 15 Summary

Agents are natural fit with open environments

Agent abstractions support expressing requirements in a natural manner

Agents go beyond objects and procedural programming

chapter 15: agents service-oriented computing: semantics, processes, agents – munindar p. singh...

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