Transcript Chapter 15: Agents - NC State Computer Science

Chapter 15:
Agents
Service-Oriented Computing: Semantics, Processes, Agents
– Munindar P. Singh and Michael N. Huhns, Wiley, 2005
Highlights of this Chapter
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Chapter 15
Agents Introduced
Agent Descriptions
Abstractions for Composition
Describing Compositions
Service Composition as Planning
Rules
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What is an Agent?
The term agent in computing covers a wide
range of behavior and functionality
 An agent is an active computational entity
 With a persistent identity
 Perceives, reasons about, and initiates
activities in its environment
 Communicates (with other agents) and
changes its behavior based on others
 Business partners => agents
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Agents and MAS for SOC
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Why agents for services?
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Unlike objects, agents
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Autonomy, heterogeneity, dynamism
Are proactive and autonomous
Cooperate or compete
Model users, themselves, others
Dynamically use and reconcile ontologies
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Modeling Agents: AI
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Traditionally, emphasize mental
concepts
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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
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Modeling Agents: MAS
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Emphasize interaction
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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
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Mapping SOC to Agents
Agents as components of an open system
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Autonomy => ability to enter into and
enact contracts; compliance
Heterogeneity => ontologies
Loose coupling => communication
Trustworthiness => contracts, ethics,
learning, incentives
Dynamism => combination of the above
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A Reactive Agent
Reactive
Agent
Sensors
inputs
percepts
ConditionAction Rules
Environment e;
RuleSet r;
while (true) {
state = senseEnvironment(e);
a = chooseAction(state, r);
e.applyAction(a);
}
Perceive
Environment
world
model
Environment
Select Action
action
outputs
Effectors
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A Rational Agent
Rationality depends on ...
A performance measure, e.g., expected utility
 What the agent has perceived so far
 What the agent knows ahead of time
 The actions the agent can perform
An ideal rational agent: for each possible
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percept sequence, it acts to maximize its
expected utility, on the basis of its knowledge
and the evidence from the percept sequence
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Logic-Based Agents
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An agent is a knowledge-based system
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Explicitly represents symbolic model of the world
Reasons symbolically via logical deduction
Challenges:
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Maintaining adequate descriptions of the world
Representing information about complex realworld entities in symbolic terms
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Easier in information environments than in general
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Cognitive Architecture for an Agent
Agent Alice
Beliefs, Desires, Intentions
Sensors
Agent Bob
Communication
Infrastructure
Beliefs, Desires, Intentions
Sensors
Perceptions
Perceptions
Communication
Interfaces
Reasoner
Reasoner
Effectors
Effectors
Actions
Actions
For SOC, sensors and effectors are services;
communication is via messaging middleware
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Generic BDI Architecture
Sensor
input
A BDI architecture
addresses how beliefs,
desires and intentions
are represented,
updated, and acted
upon
brf
beliefs
Generate options
desires
filter
intentions
action
Action
output
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Architecture of BDI-Based Agent
Environment
AgentSet
Action
-a : AgentSet
-elements: Vector
+run()
+applyAction(in a : Action)
+add(in a : Agent)
+remove(in a : Agent)
Agent
BeliefSet
-elements: Vector
+includeObservation()
Belief
-id: String
-value: String
-B : BeliefSet
-D : DesireSet
-P : IntentionSet
-I : Intention
-e : Environment
-name: String
-a : Action
+run()
+currentIntentionIsOK() : boolean(idl)
+stopCurrentIntention()
+chooseIntention()
+perceiveEnvironment()
+takeAction()
IntentionSet
-elements: Vector
+getApplicable(in D : DesireSet, in B : BeliefSet) : IntentionSet
Execution Cycle: the agent
1. Receives new information
2. Updates beliefs and goals
3. Reasons about actions
4. Intends an action
5. Selects an intended action
6. Activates selected intention
7. Performs an action
8. Updates beliefs, goals,
intentions
DesireSet
-elements: Vector
Intention
+getApplicable(in B : BeliefSet) : DesireSet
Desire
-id: String
-priority: int
+context(in B : BeliefSet) : boolean(idl)
Chapter 15
-id: String
-priority: int
-d: Desire
-a : Agent
+satisfies(in d : Desire) : boolean(idl)
+execute(in a : Agent) : boolean(idl)
+context(in B : BeliefSet) : boolean(idl)
+stopExecuting()
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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 of services
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Based on their inputs, outputs,
preconditions, and effects
Used for composition and interoperation
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OWL-S Service Ontology
Resource
provides
Service
supports
presents
describedBy
ServiceGrounding
ServiceProfile
ServiceModel
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OWL-S Compared to UDDI
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provides
Resource
Service
presents
describedBy
ServiceProfile
OWL-S
Service
Model
supports
ServiceModel
ServiceGrounding
input
QualityRating
precondition
Process
hasProfile
ParameterDescription
Profile
effect
Actor
ServiceCategory
computedInput
computedOutput
AtomicProcess
realizes
SimpleProcess
expand
CompositeProcess
ProcessComponent
Sequence
components
computedPrecondition
computedEffect
invocable
composedBy
Chapter 15
ServiceParameter
output
ControlConstruct
Split
...
RepeatUnit
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OWL-S Example: Processing Book Orders
Book Store
Sequence Process
Selection Process
Create
Account
Load
Account
Iteration Process
Choice Process
Add to
Order
Choice Process
Charge
Credit Card
Select
Credit Card
Choose
Book
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OWL-S IOPEs for Bookstore Example
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Composition as Planning
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Service composition as planning:
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Chapter 15
Represent current and goal states
Represent each service as an action (with
inputs, outputs, preconditions, effects)
Represent a composed service as a plan
that invokes the constituent services
constraining the control and data flow to
achieve the goal state
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Rules: Logical Representations
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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
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Compare with relational calculus (classical SQL)
or description logics (OWL)
Declarative, although imperfectly so
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Kinds of Rules
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ECA or Reaction
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Derivation rules: special case of above
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On event if condition then perform
action
Integrity constraints: derive false if error
Inference rules
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If antecedent then consequent
Support multiple computational strategies
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Forward chaining; backward chaining
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Applying ECA Rules
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Capture protocols, policies, and
heuristics as rules
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Often, combine ECA with inference
rules (to check if a condition holds)
Modeling challenge
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Examples?
What is an event?
How to capture composite events by
pushing event detection to lower layers
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Applying Inference Rules
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Inference rules capture general requirements
well
Elaboration tolerance requires defeasibility
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Leads to logical nonmonotonicity
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Write general rules
Override them as need to specialize them to
account for context
Easy enough operationally but difficult to
characterize mathematically
Details get into logic programming with negation
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Use of Variables
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Need free variables to make the rules
generic in how they apply
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For ECA rules: event and condition
For inference rules: antecedent
Should generally not have free variables in
consequent to ensure “safety”
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Free variable in action indicates perform action
for each binding
Free variable in consequent means assert it for
each binding
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Chapter 15 Summary
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Chapter 15
Agents are natural fit with open
environments
Agent abstractions support expressing
requirements in a natural manner
Agents go beyond objects and
procedural programming
Self-study Jess
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