Tutorial 1: Tutorial 1: CAMLE: Caste-centric Agent-Oriented CAMLE: Caste-centric Agent-Oriented Methodology of Software DevelopmentMethodology of Software Development－－－－ Meta-model, Languages and EnvironmentMeta-model, Languages and Environment

Hong ZhuDept. of Computing and Electronics

School of TechnologyOxford Brookes University, Oxford, UK

Email: hzhu@brookes.ac.uk

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OutlinePart 1: Background

– What is agent-orientation?– Why agent-orientation?– Overview of agent-oriented software engineering

Part 2: CAMLE methodology– Meta-model: conceptual model of agent-oriented systems– Modeling: language CAMLE and modeling environment– Specification: Language SLABS– Programming:

• Agent/object oriented programming in SLABSp • Pure agent-oriented programming in CAOPLE

– Language– CAVM: virtual machine

Conclusion– Future research directions

Part 1: Background

1. What is agent orientation?– Illustrate through an example

• Solution in structured approach• Solution in object-oriented approach• Solution in agent-oriented approach

More details will be covered in part 3

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Introductive ExampleSummer School

A summer school is organised to teach a number of classes. Some classes are scheduled to be hold at the same time, but, of course, at different classrooms. Students can choose the classes to attend. Students may be unfamiliar with the site where classes are. The question is how to make sure that students go to the right classes.

Example adapted from:

Design Patterns Explained, by Shalloway, A. & Trott, J., 2002

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Structured Programming (Stepwise Refinement)

1. Get a list of people in the class

2. For each person on the list Do:① Find the next class he/she is attending

② Find the location of the class

③ Find the way to get from your classroom to the person’s next class

④ Tell the person how to get their next class

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Structured Analysis and Design

Get student

list

Find next class

Find class

location

Find route to

class

Give directionClass lists Student

schedules

Site maps

Class locations

Instructor

Student

List of students

Class

Student

Next class

Next Class

Location

Map

Route

Location_to

Location_from

Route, Next class

Next class

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Object-Oriented Approach

Tc: TClass

St: Student

Mp: Map

1. GoFrm(Tc)

2. FindRoute(Tc, nxt)

InstructorTClass

Location

Time

Student

Schedule: *TClass

GoFrm(cl: TClass)

…

Map

FindRoute(st,fn)

Change of responsibility in finding out the route to the next classroom from the instructor to the students

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Agent-Oriented ApproachInstructors

Students

Organiser

Global schedule Teach

Local knowledge

Set

SetAccess

Inform

Inform

Access

Access

Access

Change of responsibility on students’ behaviour from Summer School to students themselves.

Expected Students’ Behaviour

sStudent. [ time: AttendClass(C)]

if time is before GlobalSchedule.C.start-time

2. Why Agent-orientation• Problems in current methodologies:

– Analysis of OO methodology

• Requirements of the new methodology: – Analysis of the problem domain and solution space

Problems nicely

represented in a methodology

Problems mapping into

a solution awkwardly

Solutions directly supported by technology & infrastructure

New technology and infrastructure

Why need a new methodology?

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OO Philosophy• A good information system’s structure should

reflect the structure in the real world in order for the system to be easy to understand, easy to develop and to maintain and easy to reuse.

Isomorphism HypothesisAn information system can and should be constructed with a structure that reflects the real world’s structure.

• Everything is an object.Universal Hypothesis

An information system can and should be constructed from objects and nothing else.

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Conceptual Model of Object-OrientationStructural Aspect of OO

(Simplified UML Metamodel)

Model Element

Classifier RelationshipInstance

Class

Feature

Structural Feature

Behavior Feature

Method Attribute

Association Generalization

Association end

Whole-part

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Dynamic Aspect of OO

• An object executes a method if and only if the method is called (i.e. a corresponding message is received);

• The method to be executed when a messages is received is determined at run-time according to inheritance relation (dynamic binding);

• An object is created and destroyed by other objects.

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An Analysis of OO Meta-Model • In the world of a zoo, we have objects of tigers, rabbits

and carrots. However, in an OO information system,

– The rabbit cannot move to find carrots unless there is a ‘remote controller’;

– A rabbit cannot see if there is a tiger unless the tiger sends a message to the rabbit;

Structural mismatch:Need additional elements in the system.

Behavioural mismatch:Need additional behaviour rules.

The problem is in the meta-model.

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Problem Domain and Solution SpaceDevelopment Practice

Out-sourcing, COTS, Open source, Service orientation

Infrastructure & Technologies Internet and Web technology

(Grid, P2P, Cloud, WS, Semantic web, etc.)

Wireless and mobile computing and communication

Pervasive and wearable computing Agent technology (e.g. ontology,

ACL, protocols) Multiple core CPU hardware

Novel applications• E-commerce, • E-science, • E-government,• Online entertainments,• Online education,• Virtualisation,• Pervasive computing,

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Challenges to MethodologyCurrent State

• System centric Development targets at the construction of a self-contained functionally complete system

• Control centric Design concentrates on how components control each other

• Human centricCommunication between developers is assumed, and its effectiveness is the central problem of software methodology

• Reliability centricReliability as the most important quality criterion of software systems

• Reuse centric Reusability as a most desirable feature, and the main hope of improving software productivity

New Requirements• Service centric

Development targets at provide a set of services, which may be not a system in traditional sense

• Cooperation centric Design focuses on how a service cooperate with others, even the unknowns

• Run-time centricCommunication between developers cannot be assumed, it is replaced by communications between components at run-time

• Robustness centricHow to continue services in the presence of failures become more and more important

• Evolution centric Sustainability for long term evolution becomes the focus

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New Features of Computational Entities

• Autonomous resources– Different owners of resources

• Proactive behaviour– Hollywood model:

‘You don’t call me. I’ll call you’– Watch the environment, then

take appropriate actions• Collaborative interaction

– Complicated interaction protocol– Search-binging-invocation

• Persistent lifespan – Continuously running– Last forever

Agent!

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3. Overview of Agent Orientation

• Models of agents

• Agent-oriented software development methodologies

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Various agent models• Mentalistic models

– An agent consists of a number of aspects of mental states and behave according to the state of its mental state and its view of the environment

• Game theoretic models– An agent and the system consists of a number of

variables a utility function defined on the variables and the agent take actions to maximise the utility

• Reactive models– An agent senses the changes in its environment and

take actions according to a set of predefined rules.

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Mentalistic models of agents• BDI agent has the mental state variables of

– Belief: the agent’s view of the environment. According to its local information of the environment, it beliefs the system is in certain state;

– Desire: the states of the environment and the agent that it want to be in.

– Intension: the state of the system and the agent that it want to be in by taking certain sequence of action in the relatively near future.

• Other mental state variables: – Goal: the state of the system and the agent that the

agent wants to be in eventually; – Plan: the sequence of actions that the agent will take

in the immediate future;

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Genealogy of AO methodologies

Henderson-Sellers, B. and Giorgini, P., Agent-Oriented Methodologies, Idea Group, 2005, Page 7.

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Agent-oriented SE Methodologies• Gaia (Zambonelli, Jennings, and Wooldridge, 2003 )

– Based on organization-oriented abstraction in which software systems are conceived as organized society and agents are seen as role players. The implementation is towards object-oriented. No languages at all.

• Tropos (Bresciani, Giorgini, Giunchiglia, Mylopoulos and Perini, 2004)– Uses of notions related to mental states like belief, intention, plan,

goals, etc., to represent the abstraction of agent’s state and capability.

– The implementation is based on AI technology. • i* (Yu, E. et al.)

– Focus on requirements analysis using agent concepts. – Notation for requirements specification.

• AUML (Bauer, Muller, and Odell, 2001)– Extension of UML notation with notation to represent agents and

agent classes. – In lack of a well-defined semantics and meta-model. Currently, it

only has a static meta-model.

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Part 2

CAMLE Methodology

Caste-centric

Agent-oriented

Methods

Languages and

Environments

24/3/2009 23Caste-Centric Meta-Model of MAS

Maintenance & Testing Tools

(AquIS)

Modelling Tools & Env.

Programming Env. & Virtual

Machine (CAVM)

Formal Reasoning (Scenario Calculus)

Modelling, Analysis &

Design(CAMLE)

Specification (SLABS)

Implementation& Programming

(SLABSp, CAOPLE)

Development process model(Growth Model)

MAS Architecture of Growth Environment

Development of Web Services

Meta-Level

Method-Level

Tool-LevelOverview of CAMLE Methodology

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A brief history of caste-centricPre-CAMLE Agent-based framework, process and tools for quality assurance of web-based applications

1999-2000 Caste-centric metamodel and SLABS language (FAABS 2000, IJSEKE 2001, MAMA 2000)

2002 Informal modelling notation (ICFEM 2002, AOIS 2002)

2003

Dynamic caste language facility and enhance SLABS specification language (AAMAS 2003)

CAMLE modelling language (GCC 2003, IAT 2003)

Inception of agent-oriented programming language (JMLC'2003)

2004

CAMLE modelling environment (COMPSAC 2004, SELMAS 2004)

SLABS’ application to Web Services (GCC 2004)

Programming language SLABSp (APSEC 2004)

2005

CAMLE’s application to Web services (WORDS 2005)

Scenario calculus (SEKE 2005)

Scenario calculus applied to formal analysis of agent communities

2006Adaptive caste mechanism (AOSDM’06@SEKE 2006)

Framework and methodological principles of agent-oriented information systems

2007

Formal approach to engineering emergency (EEDAS 2007, CEC 2007)

Design programming language CAOPLE

Design and implementation of virtual machine CAVM (SEKE 2008)

2008 Case study of caste-centric methodology in healthcare MAS

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1. CAMLE Meta-model

• Structure and static features– Agent– Caste– Multi-agent systems and Environment

• Behaviour and dynamic features– Communication mechanism– Behaviour rules

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Meta-Model of Caste-centric Agents

Caste-Centric Meta-ModelAgent = <Data, Operations, Behaviour>Environment

Castes = { agents | structure& behaviour property}

MAS = {Agentn}nI

Environmenttime(Agent, MAS) MAS – {Agent}

Communication( A to B)

= A.Action + B.Observation

Classifier

Caste

State space

Action space

Behavior rules

Environment description

Relation

Inheritance

Migration

Aggregation

Agent

ClassObject

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Agent

Agents are active computational entities that situate in their designated environments and encapsulate

– Data: the state of the agent (visible, or internal) – Operations: the actions that an agent can take

(visible or internal) – Behaviour: the rules the control the agent’s

actions and state changes

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Description of Agents in SLABS

Visible state-variables and actions

Invisible state-variables and actions

Behaviour-specification

Name: castes (Instantiation)

Environment description

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Student’s Behaviour

Beginning of school

Setup schedule

Select next class

Find class location

Work out route

Attend class

Time to class

In right mode

Not fancy any class

Have a rest

Time to class

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Another Student’s Behaviour

Beginning of school

Make friends with

other students

Time to class

Follow a friend to the class

FriendGo to Class

FriendGo to Pub

Follow the friend to the pub

Buy friend a drink

Time to class

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CasteA caste is a set of agents that have same structural and behavioral characteristics.

•Multiple classification: – An agents can be a member of a number of castes.

•Dynamic classification: – Agent can change their caste membership at run-time by

join a caste or quit from a caste

•Autonomous part-whole relationship: –Aggregate: Independent but collaborative–Composite: Parts are cooperative, but controlled by the

whole–Congregation: Parts are autonomous, cooperative and

rely on the whole to benefits from the membership.

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Description of Castes in SLABS

Visible state-variables and actions

Invisible state-variables and actions

Behaviour-specification

Name <= castes (instantiation)

Environment description

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Designated Environment• Explicitly specification of environment by

declaring which agent is in its environment– All: Caste -- All the agents in the caste– Agent: Caste -- A specific agent of the caste– Var: Caste -- A variable agent in the caste

• An agent can change its environment – By joining a caste– By quitting a caste– By changing the value of environment variables

• The environment of an agent can also change beyond the agent’s control– Other agents join or quit a caste that is a part of the

agent’s environment • The environment is not completely open, not

closed, not fixed.

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2. Specification Language SLABS

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Specification of Behaviours in SLABSFormat of behaviour rules:Behaviour-rule ::= [<rule-name>] pattern |[ prob]event,

[if Scenario] [where pre-cond];

Pattern Meaning

$ The wild card, which matches with all actions

Silence

X Action variable, which matches an action

Act (a1, ...ak)An action Act that takes place with parameters match (a1, ...ak)

[p1,..., pn] The previous sequence of events match the patterns p1, ..., pn

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Scenario Descriptions in SLABSA scenario is a combination of a set of agents’ behaviours and states that describe a global situation in the operation of the system.

Scenario Meaning

Predicate The state of the agents satisfies the predicate

A=B (or AB) The identifiers A and B refer to the same (or different) agent

AC Agent A is in the caste C

A:P Agent A's behaviour matches pattern P

XC.Sc The scenario Sc[X/A] is true for all agents A in caste C.

[m]XC.Sc There are m agents in caste C such that Sc[X/A] is true, where the default value of the optional expression m is 1.

S1 & S2 Both scenario S1 and scenario S2 are true

S1 S2 Either scenario S1 or S2 or both are true

S Scenario S is not true

{XC | Sc} The set of agents in caste C such that Sc[X/A] is true

XC.Sc The number of agents A in caste C such that Sc[X/A] is true

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3/25/2000

Examples of Scenarios

(1) Maze: !n, m{1,..,10}. Bean(n, m)=False.

It describes the situation in the mice-maze system when there is no bean left in the maze.(2) pParties.t2000:[nominate(Bush)] || t2000=(4/2000).

It describes the situation that at least one agent in the class Parties took the action nominate(Bush) at the time of April 2000. (3) ( x Citizen.[vote(Bush)] / x Citizen.[$]) > 1/ 2

It describes the situation that more than half of the agents in the class Citizen took the action of vote(Bush).

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Examples of Rules[!SpareMoney>£2000] Buy(ShareX);

If (XBroker.[Buy(ShareX)]) (XBroker.[$])/2

[!SpareMoney£2000 & SpareMoney £500] Save(SpareMoney, BankX, AccoundTypeY);

if BankX:[!AccountTypeY_Rate=Z]

& WBanks.W:[!AccountTypeU_RateZ]

[!SpareMoney<£500] Spend(SpareMoney, BarX);

if GCMS.G:[Suggest(BarX)].

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3. Modeling Language and Environment CAMLE

• Language– Collaboration diagrams– Caste diagrams– Behaviour + Scenario diagrams

• Tools and Environment– Model construction tools– Consistency checkers– Transformation from models to formal

specifications

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Example: Caste Diagram

University Member

Student Faculty

Undergraduate Postgraduate PhD student

Alumni

Staff Manager

Module Manager

Department

Secretary

University

Aggregation

Composition

Congregation

Migration

Participation

Inheritance

Caste Caste node

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Examples: Collaboration Diagrams

Undergraduates

Faculties

Give Lecture

PhD Students

Practical Class

Personal Tutor: Faculties

Academic Advice

Request Reference

Supervisor: Faculties Suggest

research topic

Report progress

Action Announce_Module_Result, Submit_Class_List, Assign_Teaching_Task, Report_Accomplishment

Module Manager

Staff Manager

Students

Faculties

Head of Dept: Faculty

Department

Report_Accomplishment

Instruction

Announce_Module_Result

Select module

Report_Exam_Results

Submit_Class_List

Assign_Teaching_Task

Notify(Module_list,Exam_Results)

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Example: Behaviour Diagram

Apply Postgraduate

course

Status=Final YearAverage = ‘A’

Request reference

Personal Tutor: Faculties

Agree as referee

Postgraduate course available

CS: Dept Office

Offer Postgraduate course

JOIN(Postgraduates);QUIT(Undergraduates)

Behaviour of a final year student

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Modeling Environment

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Architecture of Modelling Environment

Diagram Editor

Partial Diagram

Generator

Well-formedness

Checker

Users’ Requirements

Graphic Models

Graphic User Interface

Model Manager

Formal Specifications

Specification Generator

Consistency Checker

Controller

Collaboration Model Checker

Caste/Collaboration

Checker

Behaviour/Collaboration

Checker

Caste/Behaviour Checker

Behaviour Model

Checker

General/ Specific Checker

Cross level Checker

Check Result

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4. Application to Web ServicesThe basic feature of Web Services:• autonomous

Each web service provider controls its own resources and in charge of its own behaviour

• active and pro-activeWS components are not just waiting for another elements to call. They may make initiative actions

• persistent computational entities: WS components are supposed to run continuously and retain their state over a long period

• sociableWS dynamically discovers other web services and establish links between them at runtime

Basic features of WS matches the characteristics of agents at the highest abstraction level, but do not match many concrete models of agents, such BDI and game models.

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Caste-Centric View of WSEach web service provider/requester can be considered as an agent, in particular

– Visible state as the information published on the Internet

– Invisible stateas the internal state of the service

– Visible Actionsas the provided services

– Invisible actionsas the events internal to the implementation of the service

– Behaviour rulesthe code that determines the way that the web service fulfils its tasks

Note: an agent can be implemented as a compound agent, i.e. it consists of several agents.

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Modelling WS Application Systems

• From service provider’s perspectives– how developers of service provider model the service

provider system • Define the functionality and usage of the services without give

away unnecessary implementation details• Define the assumptions on the usages of the services without

over restricting the development of the users of the services

• From service requester’s perspectives– how the design knowledge of the service provider

specified in the model are used by the developers of the requesters

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Service Provider’s Perspective Step 1: identify the types of agents participate

in the operation of the system and specify them as castes

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Step 2: Identify the communications between service providers and requesters and represent the results in general collaboration diagrams

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Step 3: Identify the scenarios in which service providers and requester collaborate with each other and represent the scenarios in scenario-specific collaboration diagrams– Scenario 1: to set up an auction for a seller

While collaboration diagrams are expressive enough to describe workflows in the form of action sequences, it is not capable of expressing the semantics of business rules.

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– Scenario 2: to run an online auction to sell the item to buyers

Sub-scenario of bid successful

Sub-scenario of bid failed

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Step 4: Identify and specify behaviours rules for the service provider and represent them as behaviour diagrams for the provider caste

Example: Behaviour rules for auction service providers in the interaction with buyers. When a buyer requested to join the auction, its credit must be checked

and the membership issued if its credit is OK; When receives a bid from a member buyer, it must acknowledge of the

receipt of the bid with a unique bid identifier; Every received bid must be compared with the current best bid. If the

new bid beats the current best bid, the new bid becomes the current best bid; otherwise, a failure message is sent to the bidder;

By the scheduled finish time of the auction, an acceptance message must be sent to the bidder of the best bid;

Payment from the bid winner must be cleared and fund transferred to the seller with commission charged with the agreed commission rate.

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Rule: When a buyer requested to join the auction, its credit must be checked and the membership issued if its credit is OK.

Example: A Rule of Provider Caste

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Step 5: identify and specify the assumptions on the service requesters’ behaviour and also represent them as behaviour diagrams for their castes

Example: Behaviour rules for buyers in the interactions with the auction service provider.

• A buyer must join an auction before the scheduled start date of the auction and become a member of the auction before it submits any bid;• A buyer’s bid for an item must be better than the current

best bid for the item;• By the scheduled finish time of the auction, only the best

bid is accepted and its buyer must buy the item;• If a buyer’s bid is beaten by another bid (from another

bidder or from the same buyer), the beaten bid is failed, which means the buyer cannot buy the item;

• A buyer can quit from the auction only if after its bid failed.

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Note• The complete protocol for the interaction between

buyer and the auctioneer is expressed as two sets of rules. – One is for the auctioneer, and – One for the buyers

• The model from the service provider’s perspective consists of the following– A caste diagram describes the architecture of the

system from provider’s view– A set of collaboration diagram describes

communications and scenarios– A set of beahviour diagram, one for each caste

• the Buyer caste, the Seller caste, and the auction provider

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Service Requester’s Perspective

Example: Consider an online flight ticketing service that sells air tickets of an airline via an e-commerce web site.

Business rules:– Example: to sell the unsold tickets by online auction when

the time reaches 7 days before the scheduled date of flight.

– The normal business rules and process of the software is specified as a caste called TicketSeller.

Architectural solutions:– Agents that sell tickets by online auction must also obey the

auction protocol.

– Two solutions: sub-caste vs dynamic casteship

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Solution 1: Sub-Caste

• A new caste SellByAuction is created

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Solution 2: Dynamic Casteship• Agents of the TicketSeller can join the

caste Seller

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Overall Structure of CAMLE Models of WSModel for Developing Service A

Model of the agents that provide service A

Model of the agents that request service A

Model of the agents that implement the internal business logic of service A

Model of the agents that provide service B

Model of the agents that provide service C

Model of the agents that implement the internal business logic of service CModel of the agents that

request service CModel for Developing

Service B

Model of the agents that implement the

internal business logic of service B

Model of the agents that request service B

Model for Developing Service C

Modelling and specification of WS is not just a problem of defining the interface + workflow. It is more closely related to semantics that are beyond ontology.

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5. Programming Language SLABSp• To gain experiences in directly implementing

MAS in an agent-oriented programming language as a key step toward a new paradigm

• Experiments with the design and implementation of agent-oriented programming languages based on the caste centric meta-model – To test the feasibility of the concepts and language

facilities of agent-oriented programming language– To test the caste-centric agent-oriented programming

style

See (Wang, Shen & Zhu, 2004, 2005a, 2005b) for details.

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Structure of SLABSp Programsagent ::= (Java-Import)* ‘agent’ name [‘extends’ name (‘,’ name)*]

‘{’ (element | Java-Definition)* ‘}’caste ::= (Java-Import)* ‘caste’ name [‘extends’ name (‘,’ name)*] ‘{’ (element | Java-Definition)* ‘}’element ::= state-element | action-element | behavior-element

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state-element ::= [‘internal’] ‘state’ type-id id ‘(’ parameter-list ‘)’ ‘{’ (Java-Definition | getf | setf )* ‘}’getf ::= ‘get’ ‘{’ Java-Code ‘}’setf ::= ‘set’ ‘{’ Java-Code ‘}’

action-element ::= [‘internal’] ‘action’ id ‘(’ parameter-list ‘)’ ‘{’ ‘do’ ‘{’ Java-Code ‘}’ (Java-Definition)* ‘}’

behavior-element ::= ‘behavior’ id ‘{’ ‘do’ ‘{’ Java-Code ‘}’ ‘}’ ‘when’ ‘{’ pattern ‘}’ ‘while’ ‘{‘ scenario ‘}’

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scenario ::=agent-id ‘:’ pattern| relation-expression| ‘for’ (number | ‘all’) caste-id `:’ pattern| scenario ‘and’ scenario| scenario ‘or’ scenario| ‘not’ scenario

pattern ::= ‘[’ sequence-unit (‘,’ sequence-unit)* ‘]’sequence-unit ::=

action-pattern| ‘!’ state-assertion

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Example 1import java.lang.*;caste Worker{ state int flag() { int v = 0; get {return v;} set {v = value;} } action sleep(){ do{ System.out.println(getAgentName() + " : I'm so tired, ~zZ"); try{ Thread.sleep(10*1000); // 10 seconds }catch(InterruptedException e){ } } } action work(){ // work hard do{ System.out.println(getAgentName() + " : I'm full of energy! #####"); } } behavior sleep(){ do{ state flag() = 1; action sleep(); } }when{ [! state flag() == 0] } while{ } behavior work(){ do{ state flag() = 0; action work(); } }when{ [state flag() == 0] } while{ }}

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Example 2

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Overview of the implementation of SLABSp

SLABSpLibrary

SLABSp Source(* .p)

J ava Source(* .java)

J ava AgentComponents

SLABSp Compiler

J ava Bytecode(* .class)

J ava Compiler

J ava Virtual Machine

SLABSp Runtime Platform

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JacPlatform

JacAgentContainer

JacAgent

JacCasteContainer

JacCaste

Runnable<<Interface>>

Runtime Platform• Based on JVM

Platform

Naming

Distribute

Security

Codebase

Distribute

Lifecycle

Container

Communicate

Evolution

Administration

More details can be found in (Ji, Shen & Zhu, 2004, 2005a)

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Dynamic execution process

<<Thread>>

Agent

behaviorbehavior

Behavior rule

The agent periodically checks condition of its behavior rules.

ScenarioScenarioScenario

The condition of the behavior rules are

defined in terms of environment

scenarios.

Agent CasteAgentAgent

The evaluation of a scenario depends on the states and actions of the agents in the environment.

ActionActionAction

An action will be taken if the condition of a behavior rule is satisfied.

CasteCaste

Environment

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6. Pure Agent-oriented programming language

CAOPLE

• The language CAOPLE

• Virtual machine CAVM

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Example: A Simple CAOPLE Program (1)

caste Peer;observes all p in Peer;action say(word: String) { };Init say(“Hello”);

endcaste Peer.

caste FriendlyPeer <=Peer;body

when exist p in Peer: [say(“Hello”)]-> then say(“Welcome”);end;

endcaste FriendlyPeer.

caste CheerfulPeer <=Peer;body

when exist p in Peer: [say(“Hello”)]-> then say(“Hi, good morning.”);end;

endcaste CheerfulPeer.

Heterogeneous behaviours in response to an event

Network transparency: • Multiple agents running

on computers over a network

• Code deployed on different computers

Main features:

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caste Monitor;observes all p in Peer;var MessageCount: Integer;init MessageCount := 0;body when exist p in Peer: [say(x)] ->begin

MessageCount := MessageCount + 1; endend

endcaste Monitor

Example: A Simple CAOPLE Program (2)

caste Peer;observes all p in Peer;action say(word: String) { };Init say(“Hello”);

endcaste Peer.

caste Display<= Textbox;observes all p in Peer;var m: String;body when exist p in Peer: [say(m)] ->begin

Output( AgentID(p)# “ says: ” # m); endend

endcaste Display

Separation of concerns:• Computation algorithm• Input/output• Book keeping

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Example: A Simple CAOPLE Program (3)

caste Peer;observes all p in Peer;action say(word: String) { };Init say(“Hello”);

endcaste Peer.

caste SmartPeer <=Peer; Observes LocalDateTime: DateTime;

bodyWhen

LocalDateTime.Day = Monday -> Join (FriendlyPeer);

LocalDateTime.Day <> Monday -> Join (CheerfulPeer);end;

endcaste SmartPeer.

Adaptive behaviour • An agent can

autonomously join and quit a caste to change its role and so to change its behaviour

• An agent’s behaviour can be context sensitive to the change in its environment

Context and environment

Adaptation of behaviour according to the context

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Overview of CAOPLE LanguageProg ::= {<TypeDec> }* {<CasteDec>}*<TypeDec> ::= type <TypeExp> {; <TypeExp>} end<TypeExp> ::= <PrimitiveType>|<StructureType>

<CasteDec> ::= caste <CasteName>[<parameters>] [Inheritances]; [<EnvironmentDecs>] [<StateDecs>] [<ActionDecs>] init <statement>; body [<LocalDecs>;] <Statement> endcaste <CasteName>

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<EnvironmentDecs> ::= observes {<EnvDec> ; }<EnvDec> ::= <AgentId> | all <ID> in <CasteID> | var <IDList> in <CasteID> [:=<AgentID>] | set <IDList> in <CasteID> [:=<AgentSetExp>]

<StateDecs> ::= { <StateDec> ; }<StateDec> ::= var <IDList>:<Type>[:= <ConstExp>]

<ActionDecs> ::= {<ActionDec> ; }<ActionDec> ::= action <IDList> [ ( <ParameterList> )] [<ActionBody>]<ActionBody> ::= <Statement>

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<Statement>::= <Assignment> | begin <Statement> {; <Statement>} end | <ActionEvent> | <CasteEvent> | <AgentEvent> | <WhenStatement> | <withStatement> | <LoopStatement> | <ForAllStatement> | <IfStatement> | <CaseStatement>

<WithStatement> ::=

with <Exp> do <Statement> end

<WhenStatement> ::=

When {<Scenario> -> <Statement> } End

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<CasteEvent> ::= join <CasteID>[( <ActuralPara> )] |quit <CasteID> |suspend <CasteID> |resume <CasteID>

<AgentEvent> ::=

create [<var> of] <CasteID>[( <ActuralPara> )] [@<URL>]

| destroy [<var>]

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7. Virtual Machine CAVM: Architecture

Computer C1

Computer C2

Computer C3

LEE1

CE2

CE1

LEE2 LEE3

Computer Cn

LEEk

Local Execution Engine

Communication Engine

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Structure of LEE

CentralProcessingUnit (CPU)

ContextRegisters

PC

Loader Program Space

List of L

oaded Castes (L

LC

)object code OC1

object code OC2

object code OCn

…

Memory Space

En

vironm

ent d

ata

Agent A1

context data

Agent Am

context data

…

List of A

gents (LoA

)

Communication Manager

local networknetwork

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Structure of CE

Publication Space

Agent A1 s/a data

Agent Ai s/a data

CommunicationManager

Membership List(of active Agents)

Receiver Dispatcher

Casteobject code

DeploymentManager

localnetwork network

…

MembershipManager

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Deploy & Execute CAOPLE Programs

Computer C1

Computer C2

Computer C3

LEE1

CE2

CE1

LEE2

Computer Cn

LEEk

CAOPLESource Code

Caste SC1 Caste SCn

CAVM Object Code

Caste OC1Caste OCn

Compile

Deploy

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Caste DeploymentThe deployment tool supports distributed deployment of object code through the internet.

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CAVM’s Messages

CELEE Register/Unregister

CELEE

observe

update

update

CELEE instanceset <member list>

<member list>

<pub space>a

b

<create or join><destroy or quit>

<env. instructions>

<upd. instructions>

<dyn. membership>

A

a A

Ab

a a a

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CAVM Instructions

• Three categories of CAVM instructions– computation instructions perform computation

and local control functions– interaction instructions deal with the

interactions between agents and castes– external invocation instructions are those

operations facilitating CAVM’s interaction with native environment, and debugging purpose

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Examples of Instructions

• …• loadcaste 0 # load a caste referenced by constant[0]• agentnew # create new agent context• agentalloc # allocate agentInfo (memory space)• dup # duplicate agentinfo at the stack top• storevar 3 # store agent's variable to localvar[3]• agentreg # prepare and push the register

message• sendmessage # send the message• agentregpost # postprocess the response• agentready # mark agent context as ready• …

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Implementation

• A prototype system of CAVM has been implemented with C/C++– LEE and CE are realized as two separate

CLR console servers– GUI Management tool– make use of .NET platform for XML

processing and messaging support

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caste Peer;action sayHello() begin //do nothing endbody sayHello()endcaste Peer

caste Peer;observes all p in Peer;action sayHello() begin //do nothing endaction welcome(P: AgentID of Peer)begin //do nothing endbody when exist p in Peer: [sayHello()]-> if (p<>self) then welcome(p);end;endcaste Peer

Peer-1:

Peer-2:

caste Monitor;observes all p in Peer;var numOfPeers: Integer;init numOfPeers := 0;body when exist p in Peer:[sayHello()] -> begin numOfPeers := numOfPeers + 1; output( “Number of Peers:”, numOfPeers); endendendcaste Monitor

Monitor-1:

Experiments with CAVM• 5 experiments

– basic functions of LEE and CE

– deployment and interaction

– performance and scalability

– centralized (localhost) vs. distributed (LAN)

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Exp. 1 and 2 - basic

• Peer-1 + Monitor-1 (Exp1: centralized; Exp2: distributed)– The performances of LEE in terms of IPmS (Instruction per Milli-second) and

MPmS increase as the agent number increases– reached the peak when the number of agents was around 20, then gradually

decreased– satisfactory scalability - performance decreases slowly as the number of

Peer-1 agents increases from 40 to 100

Exp. 1 and Exp. 2 - I nstructi ons

0

10

20

30

40

50

60

0 20 40 60 80 100

Number of Agents

Inst

ruct

ions

/ms

Exp. 1Exp. 2

Exp. 1 and Exp. 2 - Messages

0

1

2

3

4

5

0 20 40 60 80 100

Number of Agents

Mess

ages

/ms

Exp. 1Exp. 2

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Exp. 3 - scalability centralized

• Peer-2 centralized– agents are created and added into the system one by

one - up to 100 agents on one LEE.– same pattern of performance change with agent

numbers

Exp. 3 - I nstructi ons

0

5

10

15

20

25

10 20 30 40 50 60 70 80 90 100

Number of Agents

Inst

ruct

ions

/ms

Exp. 3 - Messages

00. 2

0. 40. 60. 8

1

1. 21. 4

10 20 30 40 50 60 70 80 90 100

Number of Agents

Mess

ages

/ms

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Exp. 4 - scalability distributed

• Peer-2 distributed– LEEs are hosted by 1-6 PCs connect by LAN– Each LEE hosts 100 agents, so the total agent

numbers in the system are from 100 up to 600

Exp. 4 - I nstructi ons

0

2

4

6

8

10

100 200 300 400 500 600

Number of Agents

Inst

ruct

ions

/ms

Exp. 4 - Messages

0

0. 1

0. 2

0. 3

0. 4

0. 5

0. 6

100 200 300 400 500 600

Number of Agents

Mess

ages

/ms

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Exp. 5 - execution time

• Peer-0 + Monitor-0– average performance measures over execution time

of Peer-0 and Monitor-0 (the example we used before)– observations among agents from different castes.

Exp. 5 - I nstructi ons

0

20

40

60

80

100

0 500 1000 1500 2000 2500

Executi on Ti me (ms)

Inst

ruct

ions

/ms

Exp. 5 - Messages

0

0. 05

0. 1

0. 15

0. 2

0. 25

0 500 1000 1500 2000 2500

Executi on Ti me (ms)

Mess

ages

/ms

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Conclusion• Agent-orientation provides a more powerful

metaphor for the development of information systems– Agent/object mixture approach: ‘Agents manipulates

objects’, rather than ‘everything is object’. – Pure agent-orientation approach: ‘Everything is agent’

• Agent-orientation suitable for emerging technologies and novel applications, including web services applications, pervasive computing, etc.– Shift of focus from control to cooperation

• CAMLE provides a coherent set of language facilities supports the principle of agent-orientation

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Work in Progress and Future Work• Developing the compiler of CAOPLE

programming language• Developing GUI library for CAOPLE• Tools support scenario calculus (automated

reasoning about emergent behaviours in MAS)• Implementation of CAVM for other computation

platforms such as, sensor network platform, handheld devices, mobile phones, etc.

• Agent-oriented programming for multiple core hardware platform

• Design patterns for caste centric multi-agent systems

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Acknowledgement

The work reported in this tutorial are based on collaborations with my colleagues and students at Oxford Brookes University, UK, and The National University of Defence Technology, China, which include Lijun Shan, Dr. Bin Zhou, Prof. Ji Wang, Rui Shen, Prof. Xinjun Mao, David Lightfoot, Dr. Sue Greenwood, Dr. Yanlong Zhang, Qingning Huo, Dr. Fang Wang, etc.

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References

For the list of publications on caste centric methodology, please see http://cms.brookes.ac.uk/staff/HongZhu/Publication.htm