1. Agent UML

2. It is a changes towards
Requirements
Engineering

3. Introduction – traditional approach
Software
Engineers
make
changes
Software
System
{Knowledge}
Code
Software Engineers
Requirements
Models
Design
Models
…
E.g.UMLmodels

4. Make
changes
Software
System
Auto-generate
Models
{Knowledge}
E.g.UMLmodels
• Models are reused
Introduction – current thinking

5. Introduction – our thinking
translate
Agent
Software
System
• Models are reused
Agent-oriented
Models
{Knowledge}
E.g.UMLmodels
XMLmodels
• Re-generation of the system is
not necessary
CASE
tool
Make
changes

6. The Changing Needs of Requirements
Modelling
1. Technology as enabler
 Goals are discovered; may be bottom-up
2. Networked systems and organizations
 Composite systems, but dispersed, fluid, contingent, ephemeral
 Same for responsibilities, accountability, authority, ownership,…
3. Increased inter-dependency and vulnerability
 Dependencies among stakeholders (inc. system elements)
 Impact of changes
4. Limited knowledge and control
 No single designer with full knowledge and control
5. Openness and uncertainties
 Can’t anticipate all eventualities / prescribe responses in advance
6. Cooperation
 Beyond vocabulary of “interaction” (behavioural)
 Reason about benefits of cooperation – goals, beliefs, conflicts

7.  Requirements can be organised around agents.
Agents are responsible for realising the
requirements.
 Agent is the container and business rule is the
knowledge to fill the container in the requirements
model.
 Agent and business rule complements each other in
the implementation: the former is the actor, the
latter guides it and dictates the role that it plays.
 The mapping from requirements to implementation
for both the agent and the business rule brings
traceability.
Two additional model element –
agent

8. supportsrealises
is_responsible_for
consists_of
plays
Human
Agent Business Role
Business
Rule
Business
Function
collaborates
_with
Business
Agent
Software
Agent
Business
Process
Business
Goal
Business
Class
contributes_to
Sub-Goal
relates_with
participates_in
Agents play business roles,
their behaviour is dictated by
business rules and functions
Agents collaborate to
realise business goals
by participating in
business processes
Business rules
and functions
support
business goals
Meta-model
Business rules and functions represent
functional requirements. They become
agent behaviour and class method
respectively. Classes are managed by
agents, and rules are configured for the
invocation of methods.

9. i* - agent-oriented modelling
Actors are semi-autonomous, partially
knowable
Strategic actors, intentional dependencies
Meeting Scheduling ExampleMeeting Scheduling Example
“Strategic
Dependency”
Model

10. Analysis and Design of
Agent-Oriented Systems
e.g., Wooldridge Jennings Kinny (JAAMAS
2000) “GAIA”
Analysis level
Roles and Interactions
oPermissions
oResponsibilities
• liveness properties
• safety properties
oActivities
oProtocols
 Design level
 Agent types
 Services
Modelling concepts
being driven from
programming
again?!!
• Structured Analysis
from Structured
Programming
• OOA from OOD, OOP
• AOA from AOP ??

11. What are the important concepts
for
Agent Orientation as a
Modelling Paradigm ?
Intentionality
Autonomy
Sociality
Identity & Boundaries
Strategic Reflectivity
Rational Self-Interest

12. Agent Orientation as a Modelling
Paradigm
Intentionality
Agents are intentional.
Agent intentionality is externally attributed by the
modeller.
Agency provides localization of intentionality.
Agents can relate to each other at an intentional level.
Autonomy
Sociality
Identity & Boundaries
Strategic Reflectivity
Rational Self-Interest

13. Agent Orientation as a Modelling
Paradigm
Intentionality
Autonomy
An agent has its own initiative, and can act
independently. Consequently, for a modeller and from
the viewpoint of other agents:
o its behaviour is not fully predictable.
o It is not fully knowable,
o nor fully controllable.
The behaviour of an agent can be partially
characterized, despite autonomy, using intentional
concepts.
 Sociality
 Identity & Boundaries
 Strategic Reflectivity
 Rational Self-Interest

14. Agent Orientation as a Modelling
Paradigm
Intentionality
Autonomy
Sociality
 An agent is characterized by its relationships with other agents,
and not by its intrinsic properties alone.
 Relationships among agents are complex and generally not
reducible.
 Conflicts among many of the relationships that an agent
participates in are not easily resolvable.
 Agents tend to have multi-lateral relationships, rather than one-way
relationships.
 Agent relationships form an unbounded network
 Cooperation among agents cannot be taken for granted.
 Autonomy is tempered by sociality.
 Identity & Boundaries
 Strategic Reflectivity

15. Agent Orientation as a Modelling Paradigm
Intentionality
Autonomy
Sociality
Identity & Boundaries
Agents can be abstract, or physical.
The boundaries, and thus the identity, of an agent are
contingent and changeable.
Agent, both physical and abstract, may be created and
terminated.
Agent behaviour may be classified, and generalized.
Strategic Reflectivity
Rational Self-Interest

16. Agent Orientation as a Modelling
ParadigmAutonomy
Intentionality
Sociality
Identity & Boundaries
Strategic Reflectivity
Agents can reflect upon their own operations.
Development world deliberations and decisions are
usually strategic with respect to the operational world.
The scope of reflectivity is contingent.
Rational Self-Interest

17. Agent Orientation as a Modelling
ParadigmIntentionality
Autonomy
Sociality
Identity & Boundaries
Strategic Reflectivity
Rational Self-Interest
An agent strives to meet its goals.
Self-interest is in a context of social relations.
Rationality is bounded and partial.

18. What is an agent
An agent is an hardware or software system
placed in an environment that enjoys the
following properties:
oAutonomy
oSocial ability
oReactivity
oProactivity
[Wooldridge et al, 1995]

19. A stronger notion of agent
Another stronger notion assigns to the agent also
the following properties:
 Mentalistic notions:
o Beliefs
o Desires
o Intentions
 Emotional notions:
o Trust
o Friendship
o Suspiciousness
[Wooldridge et al, 1995]

20. When agent notion is useful
 The agent notion is adaptable to many HW and SW systems but it
is particularly useful in such contexts where complexity is high
enough to make the simple notion of object insufficient to describe
the system.
 The focus is on the behaviour of agents and not on
the content of objects (attributes and methods).
 Agent-oriented programming is at an abstraction level higher than
object-oriented programming.

21. Agent-oriented programming (1)
 Agent-oriented programming (AOP) is a
specialisation of object-oriented programming (OOP)
in the way the computational system is conceived:
“The computational system is seen as composed of
communicating modules, each with its own way of handling
messages.” [Shoham, 1993]
 The (mental) state of modules (agents) consists of
components such as beliefs, capabilities and
intentions.

22. Agent-oriented programming (2)
 A computation consists of agents that:
 Inform other agents about facts
 Offer and request services
 Accept or refuse proposals
 Compete for accessing shared resources
 Collaborate for achieving common goals
OOP AOP
Basic unit Object Agent
Parameters defining state
of basic unit
Unconstrained Belief, commitments,
chioces, …
Process of computation Message passing and
response methods
Message passing and
response methods
Types of messages Unconstrained Inform, request, offer,
promise, decline
Constraints on methods None Honesty, consistency, …

24. What is Agent UML
 Agent UML is a support notation for agent-oriented
systems development.
 It consists in using the UML modeling language
and extending it in order to represent agents, their
behaviour and interactions among them.
 AUML is not restricted to using UML. Other
approaches should be used wherever it makes
sense.

25. Who is interested in AUML
 OMG Special Interest Group:OMG Special Interest Group: recommends
standards for agent technology where
appropriate (www.omg.org)
 FIPA (Foundation for intelligent physicalFIPA (Foundation for intelligent physical
Agents)Modeling Technical Commitee:Agents)Modeling Technical Commitee:
tasked with developing an AUML standard
(www.auml.org)
 Other methodologies:Other methodologies: MESSAGE, Gaia,
Tropos, Prometheus, MaSE, ...

26. Common features of agents
Agents share some common characteristics:
 Identifier
• identifies each agent in a multiagent system
 Role
• defines the behaviour of an agent into the society (es. Seller, Buyer)
 Organization
• defines the relationships between the roles (similar to human or
animal organizations such as hierarchies, markets, groups of interest
or herds)
 Capability
• specifies what an agent is able to do and under what conditions
 Service
• describes an activity that an agent can perform and is provided to
other agents

27. Representation of agents
 UML Class Diagrams can be used to represent
the static view of agents.
<<agent>> agent-name
Role
role 1, role 2, …, role n
role dynamic 1, role dynamic 2, …, role dynamic n
Organization
organization 1, organization 2, …, organization n
org dynamic 1, org dynamic 2, …, org dynamic n

28. Capabilities representation (1)
 A capability is composed of the following parts:
 Input
o What the agent must receive in input to achieve his task
 Output
o What the capability generates as a result of the work
 Input constraints
o Constraints that are expected to hold in the situation before the action specified by
the capability can be performed
 Output constraints
o Constraints hat are expected to hold in the situation after the action specified by
the capability has been performed
 Input-output constraints
o Constraints that must hold across input and output situations
 Description
o A description in natural language of the capability

29. Capabilities representation (2)
<<capability>> addition
Input
x,y:Integer
Output
s:Integer
Description
This capability makes the sum of
two integers and returns an integer
<<agent>> sum
Role
addition, subtraction
rd 1
Organization
calculator
Protocol
enter-society, exit-society
compute
<<capability>> subtraction
Input
x,y:Integer
Output
d:Integer
Input Constraint
x>=0, y>=0
Input-Output Constraint
x-y >=0
Description
This capability makes the
difference of two integers and
returns an integer
• Example of capability representation:
The agent ‘sum’ has two
capabilities expressing the fact
that he is able to make additions
and subtractions
They can be defined
using OCL or simple
logic expressions

30. Service representation (1)
 A service is composed of the following parts:
 Name
• The name of the service
 Description
• A description in natural language of the service
 Type
• The type of the service
 Protocol
• A list of interaction protocols supported by the service
 Agent communication language
• The communication languages used in this service
 Ontology
• A list of ontologies supported by the service
 Content language
• A list of content languages supported by the service
 Properties
• A list of properties that discriminate the service

31. Service representation (2)
<<agent>> sum
Role
addition, subtraction
rd 1
Organization
calculator
Protocol
enter-society, exit-society
compute
<<service>> computation
Description
This service makes an addition when
requested by the request addition
protocol and makes a subtraction
when requested by the request-
subtraction protocol
Type
computation
Protocol
request-addition
request-subtraction
Agent Communication Language
FIPA ACL
Ontology
computation ontology
Content Language
FIPA SL
• Example of service representation:
The agent ‘sum’ exports a service that
makes additions and subtractions on
demand

32. Representing interactions
 Agent interactions can be represented in
UML standard using sequence diagrams

33. Concurrent interactions (1)
 UML has been extended in order to
represent concurrent communication acts
sent from the sender agent to the
receiver.
 a) Concurrent communication acts from
CA-1 to CA-n are sent in parallel.
 b) A selection of the n acts is sent in
parallel (zero or more).
 c) Exclusive choice: only one of the
communication acts is sent.

34. Concurrent interactions (2)
 a) An agent sends 3 concurrent CA
to another agent. The diagram can
be interpreted in two different ways:
 Every CA is processed from the
same agent/role by a different thread
of execution
 Every CA is processed by a different
role of the agent (in this case
mesages can be annotated
specifying the role)
 b) The same semantic of (a) but with
a simpler notation
 c) Choice from three different
communication act received by
three different threads (or roles)
 NOTE: each concurrent CA
can be sent to different
agents

35. Example of interaction
 The Buyer sends a request-for-
proposal to the Seller
 The Seller has three options to
choose within the deadline:
 make a proposal
 refuse (with different reasons)
 say he did not understand
 If the Seller has made a
proposal, the Buyer has the
choice to reject or to accept it
 If the last is the case, the Seller
schedules the proposal
informing the Buyer about its
the state
 The Buyer can cancel the
proposal execution at any time

36. Detailing interaction messages
 Any interaction process
can be expressed in
more detail.
 The “leveling” can
continue down until the
problem has been
specified adequately to
generate code.
 Also activity diagrams
and statecharts can be
used.

37. Roles management
 UML sequence diagrams can be used to represent
changes in agents’ role.

38. Object role in AOP
 Objects may always be included in an agent-oriented system
and can communicate with agents using message passing
methods.

39. References (1)
 Agents
 [Wooldridge et al, 1995]
Wooldridge and Jennings
Intelligent Agents: Theory and Practice
Knowledge Engineering Review
Volume 10 No 2, June 1995
Cambridge University Press
(www.csc.liv.ac.uk/~mjw/pubs/ker95/ker95-html.html)
 [Shoham, 1993]
Y. Shoham
Agent-oriented programming
Artificial Intelligence
60(1):51-92
(http://www.ncat.edu/~esterlin/c7902s02/Notes/Shoham.pdf)

40. References (2)
 Agent UML
 http://www.auml.org/
 http://www.jamesodell.com/
 http://aot.ce.unipr.it/auml/