Tutorial 1 CAMLE Caste-centric Agent-Oriented Methodology of Software

Tutorial 1: CAMLE: Caste-centric Agent-Oriented Methodology of Software Development －－Meta-model, Languages and Environment Hong Zhu Dept. of Computing and Electronics School of Technology Oxford Brookes University, Oxford, UK Email: hzhu@brookes. ac. uk 24/3/2009

Outline Part 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 24/3/2009 2

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 24/3/2009

Introductive Example Summer 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 24/3/2009 4

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 24/3/2009 5

Structured Analysis and Design Next Class Instructor Class Get Student student list List of students Class lists 24/3/2009 Find next class Location_from Next class Student schedules Next class Find Location Class class location Location_to Find route to class Map Site maps Route Give Route, direction Next class Student 6

Object-Oriented Approach Tc: TClass Location Instructor 1. Go. Frm(Tc) Time St: Student 2. Find. Route(Tc, nxt) Schedule: *TClass Go. Frm(cl: TClass) … Map Find. Route(st, fn) Mp: Map Change of responsibility in finding out the route to the next classroom from the instructor to the students 24/3/2009 7

Agent-Oriented Approach Inform Set Global schedule Organiser Set Local knowledge Inform Expected Students’ Behaviour s Student. [ time: Attend. Class(C)] if time is before Global. Schedule. C. start-time 24/3/2009 Instructors Access Teach Access Students Change of responsibility on students’ behaviour from Summer School to students themselves. 8

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 mapping into a solution awkwardly Problems nicely represented in a methodology Solutions directly supported by technology & infrastructure Why need a new methodology? 24/3/2009 New technology and infrastructure

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 Hypothesis An 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. 24/3/2009 10

Conceptual Model of Object-Orientation Structural Aspect of OO (Simplified UML Metamodel) Model Element Instance Classifier Relationship Class Feature Behavior Feature Method 24/3/2009 Association Structural Feature Association end Generalization Whole-part Attribute 11

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 runtime according to inheritance relation (dynamic binding); • An object is created and destroyed by other objects. 24/3/2009 12

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, Structural mismatch: – 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; Need additional elements in the system. Behavioural mismatch: Need additional behaviour rules. The problem is in the meta-model. 24/3/2009 13

Problem Domain and Solution Space Development Practice Infrastructure & Technologies q Internet and Web technology (Grid, P 2 P, Cloud, WS, Semantic web, etc. ) q Wireless and mobile computing and communication q Pervasive and wearable computing q Agent technology (e. g. ontology, ACL, protocols) q Multiple core CPU hardware 24/3/2009 q Out-sourcing, q COTS, q Open source, q Service orientation • • Novel applications E-commerce, E-science, E-government, Online entertainments, Online education, Virtualisation, Pervasive computing, 14

Challenges to Methodology Current 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 centric Communication between developers is assumed, and its effectiveness is the central problem of software methodology • Reliability centric Reliability 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 24/3/2009 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 centric Communication between developers cannot be assumed, it is replaced by communications between components at run-time • Robustness centric How to continue services in the presence of failures become more and more important • Evolution centric Sustainability for long term evolution becomes the focus 15

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 Agent! – Complicated interaction protocol – Search-binging-invocation • Persistent lifespan – Continuously running – Last forever 24/3/2009 16

3. Overview of Agent Orientation • Models of agents • Agent-oriented software development methodologies 24/3/2009 17

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. 24/3/2009 18

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; 24/3/2009 19

Genealogy of AO methodologies Henderson-Sellers, B. and Giorgini, P. , Agent-Oriented Methodologies, Idea Group, 2005, Page 7. 24/3/2009 20

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) 24/3/2009 – 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. 21

Part 2 CAMLE Methodology Caste-centric Agent-oriented Methods Languages and Environments 24/3/2009 22

Overview of CAMLE Methodology Tool-Level Maintenance & Testing Tools (Aqu. IS) Modelling Tools & Env. Formal Reasoning (Scenario Calculus) Programming Env. & Virtual Machine (CAVM) Method-Level MAS Architecture of Growth Environment Development process model (Growth Model) Modelling, Analysis & Design (CAMLE) Development of Web Services Implementation Specification & Programming (SLABS) (SLABSp, CAOPLE) Meta-Level Caste-Centric Meta-Model of MAS 24/3/2009 23

A brief history of caste-centric Pre-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) Dynamic caste language facility and enhance SLABS specification language (AAMAS 2003) 2003 CAMLE modelling language (GCC 2003, IAT 2003) Inception of agent-oriented programming language (JMLC'2003) CAMLE modelling environment (COMPSAC 2004, SELMAS 2004) 2004 SLABS’ application to Web Services (GCC 2004) Programming language SLABSp (APSEC 2004) CAMLE’s application to Web services (WORDS 2005) 2005 Scenario calculus (SEKE 2005) Scenario calculus applied to formal analysis of agent communities 2006 Adaptive caste mechanism (AOSDM’ 06@SEKE 2006) Framework and methodological principles of agent-oriented information systems Formal approach to engineering emergency (EEDAS 2007, CEC 2007) 2007 Design programming language CAOPLE Design and implementation of virtual machine CAVM (SEKE 2008) 2008 24/3/2009 Case study of caste-centric methodology in healthcare MAS 24

1. CAMLE Meta-model • Structure and static features – Agent – Caste – Multi-agent systems and Environment • Behaviour and dynamic features – Communication mechanism – Behaviour rules 24/3/2009 25

Meta-Model of Caste-centric Agents Classifier Relation Agent Object Caste Class Caste-Centric Meta-Model Agent = Environment Castes = { agents | structure& behaviour property} MAS = {Agentn}n I Environmenttime(Agent, MAS) MAS – {Agent} Communication( A to B) = A. Action + B. Observation 24/3/2009 State space Action space Inheritance Aggregation Migration Behavior rules Environment description 26

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 24/3/2009 27

Description of Agents in SLABS Name: castes (Instantiation) Visible state-variables and actions Invisible state-variables and actions Environment description 24/3/2009 Behaviour-specification 28

Student’s Behaviour Beginning of school Setup schedule Time to class In right mode Select next class Time to class Not fancy any class Have a rest Find class location Work out route Attend class 24/3/2009 29

Another Student’s Behaviour Beginning of school Make friends with other students Friend Go to Class Time to class Follow a friend to the class Time to class Friend Go to Pub Follow the friend to the pub Buy friend a drink 24/3/2009 30

Caste A 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. 24/3/2009 31

Description of Castes in SLABS Name <= castes (instantiation) Visible state-variables and actions Invisible state-variables and actions Environment description 24/3/2009 Behaviour-specification 32

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. 24/3/2009 33

2. Specification Language SLABS 24/3/2009 34

Specification of Behaviours in SLABS Format of behaviour rules: Behaviour-rule : : = [] pattern |[ prob]-> event, [if Scenario] [where pre-cond]; Pattern Meaning $ The wild card, which matches with all actions t Silence X Action variable, which matches an action Act that takes place with Act (a 1, . . . ak) parameters match (a 1, . . . ak) [p 1, . . . , pn] 24/3/2009 The previous sequence of events match the patterns p 1, . . . , pn 35

Scenario Descriptions in SLABS A scenario is a combination of a set of agents’ behaviours and states that describe a global situation in the operation of the system. Scenario Predicate A=B (or A B) Meaning The state of the agents satisfies the predicate The identifiers A and B refer to the same (or different) agent A C Agent A is in the caste C A: P Agent A's behaviour matches pattern P X C. Sc [m]X C. Sc The scenario Sc[X/A] is true for all agents A in caste C. 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. S 1 & S 2 Both scenario S 1 and scenario S 2 are true S 1 S 2 Either scenario S 1 or S 2 or both are true S {X C | Sc} X C. Sc 24/3/2009 Scenario S is not true The set of agents in caste C such that Sc[X/A] is true The number of agents A in caste C such that Sc[X/A] is true 36

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) p Parties. t 2000: [nominate(Bush)] || t 2000=(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). 24/3/2009 37

Examples of Rules [!Spare. Money>£ 2000] -> Buy(Share. X); If ( X Broker. [Buy(Share. X)]) ( X Broker. [$])/2 [!Spare. Money £ 2000 & Spare. Money £ 500] -> Save(Spare. Money, Bank. X, Accound. Type. Y); if Bank. X: [!Account. Type. Y_Rate=Z] & W Banks. W: [!Account. Type. U_Rate Z] [!Spare. Money<£ 500] -> Spend(Spare. Money, Bar. X); if G CMS. G: [Suggest(Bar. X)]. 24/3/2009 38

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 24/3/2009 39

Example: Caste Diagram Caste node Aggregation Inheritance Congregation University Migration Composition Participation Student Undergraduate 24/3/2009 Department University Member Alumni Postgraduate Faculty Ph. D student Secretary Staff Manager Module Manager 40

Examples: Collaboration Diagrams Supervisor: Faculties Report progress Ph. D Students Faculties Suggest research topic Personal Tutor: Faculties Give Practical Lecture Class Academic Advice Undergraduates Request Reference Department Action Announce_Module_Result, Submit_Class_List, Assign_Teaching_Task, Report_Accomplishment Students Announce_Module_Result Select module Submit_Class_List Faculties Assign_Teaching_Task Report_Exam_Results 24/3/2009 Module Manager Report_ Accomplishment Notify(Module_list, Exam_Results) Staff Manager Head of Dept: Faculty Instruction 41

Example: Behaviour Diagram Status=Final Year Average = ‘A’ Postgraduate course available Personal Tutor: Faculties Request reference Agree as referee CS: Dept Office Behaviour of a final year student Apply Postgraduate course JOIN(Postgraduates); QUIT(Undergraduates) 24/3/2009 Offer Postgraduate course 42

Modeling Environment 24/3/2009 43

Architecture of Modelling Environment Users’ Requirements Formal Specifications Graphic User Interface Specification Generator Model Manager Diagram Editor Partial Diagram Generator Wellformedness Checker Behaviour/ Collaboration Checker Caste/ Collaboration Checker Consistency Checker Controller Behaviour Model Checker Collaboration Model Checker General/ Specific Checker Caste/ Behaviour Checker Cross level Checker Graphic Models 24/3/2009 Check Result 44

4. Application to Web Services The basic feature of Web Services: • autonomous Each web service provider controls its own resources and in charge of its own behaviour • active and pro-active WS components are not just waiting for another elements to call. They make initiative actions • persistent computational entities: WS components are supposed to run continuously and retain their state over a long period • sociable WS 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. 24/3/2009 45

Caste-Centric View of WS Each web service provider/requester can be considered as an agent, in particular – Visible state as the information published on the Internet – Invisible state as the internal state of the service – Visible Actions as the provided services – Invisible actions as the events internal to the implementation of the service – Behaviour rules the 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. 24/3/2009 46

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 24/3/2009 47

Service Provider’s Perspective Step 1: identify the types of agents participate in the operation of the system and specify them as castes 24/3/2009 48

Step 2: Identify the communications between service providers and requesters and represent the results in general collaboration diagrams 24/3/2009 49

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. 24/3/2009 50

– Scenario 2: to run an online auction to sell the item to buyers Sub-scenario of bid successful Sub-scenario of bid failed 24/3/2009 51

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. Ø When a buyer requested to join the auction, its credit must be checked and the membership issued if its credit is OK; Ø When receives a bid from a member buyer, it must acknowledge of the receipt of the bid with a unique bid identifier; Ø 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; Ø By the scheduled finish time of the auction, an acceptance message must be sent to the bidder of the best bid; Ø Payment from the bid winner must be cleared and fund transferred to the seller with commission charged with the agreed commission rate. 24/3/2009 52

Example: A Rule of Provider Caste Rule: When a buyer requested to join the auction, its credit must be checked and the membership issued if its credit is OK. 24/3/2009 53

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. 24/3/2009 54

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 24/3/2009 55

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 Ticket. Seller. Architectural solutions: – Agents that sell tickets by online auction must also obey the auction protocol. – Two solutions: sub-caste vs dynamic casteship 24/3/2009 56

Solution 1: Sub-Caste • A new caste Sell. By. Auction is created 24/3/2009 57

Solution 2: Dynamic Casteship • Agents of the Ticket. Seller can join the caste Seller 24/3/2009 58

Overall Structure of CAMLE Models of WS Model for Developing Service A Model of the agents that provide service B Model of the agents that request service B Model of the agents that implement the internal business logic of service B Model for Developing Service B Model of the agents that provide service A Model of the agents that request service A Model of the agents that provide service C Model of the agents that request service C Model of the agents that implement the internal business logic of service A Model of the agents that implement the internal business logic of service C 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. 24/3/2009 59

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, 2005 a, 2005 b) for details. 24/3/2009 60

Structure of SLABSp Programs agent : : = (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 24/3/2009 61

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 ‘}’ 24/3/2009 62

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 24/3/2009 63

Example 1 import java. lang. *; caste Worker{ state int flag() { int v = 0; get {return v; } set {v = value; } } action sleep(){ do{ System. out. println(get. Agent. Name() + " : I'm so tired, ~z. Z"); try{ Thread. sleep(10*1000); // 10 seconds }catch(Interrupted. Exception e){ } } } action work(){ // work hard do{ System. out. println(get. Agent. Name() + " : 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{ } } 24/3/2009 64

Example 2 24/3/2009 65

Overview of the implementation of SLABSp 24/3/2009 66

Runtime Platform • Based on JVM Security Naming Lifecycle Platform Codebase Container Communicate Evolution Distribute Administration More details can be found in (Ji, Shen & Zhu, 2004, 2005 a) 24/3/2009 67

Dynamic execution process The agent periodically checks condition of its behavior rules. behavior Behavior rule <> Agent Action An action will be taken if the condition of a behavior rule is satisfied. The condition of the behavior rules are defined in terms of environment scenarios. Scenario The evaluation of a scenario depends on the states and actions of the agents in the environment. Environment Agent 24/3/2009 Caste 68

6. Pure Agent-oriented programming language CAOPLE • The language CAOPLE • Virtual machine CAVM 24/3/2009 69

Example: A Simple CAOPLE Program (1) caste Peer; observes all p in Peer; action say(word: String) { }; Init say(“Hello”); endcaste Peer. caste Friendly. Peer <=Peer; body when exist p in Peer: [say(“Hello”)] -> then say(“Welcome”); endcaste Friendly. Peer. caste Cheerful. Peer <=Peer; body when exist p in Peer: [say(“Hello”)] -> then say(“Hi, good morning. ”); endcaste Cheerful. Peer. 24/3/2009 Main features: Network transparency: • Multiple agents running on computers over a network • Code deployed on different computers Heterogeneous behaviours in response to an event 70

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( Agent. ID(p)# “ says: ” # m); end endcaste Display 24/3/2009 Separation of concerns: • Computation algorithm • Input/output • Book keeping caste Monitor; observes all p in Peer; var Message. Count: Integer; init Message. Count : = 0; body when exist p in Peer: [say(x)] ->begin Message. Count : = Message. Count + 1; end endcaste Monitor 71

Example: A Simple CAOPLE Program (3) caste Peer; observes all p in Peer; action say(word: String) { }; Init say(“Hello”); endcaste Peer. Context and environment caste Smart. Peer <=Peer; Observes Local. Date. Time: Date. Time; body When Local. Date. Time. Day = Monday -> Join (Friendly. Peer); Local. Date. Time. Day <> Monday -> Join (Cheerful. Peer); endcaste Smart. Peer. 24/3/2009 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 Adaptation of behaviour according to the context 72

Overview of CAOPLE Language Prog : : = { }* {}* : : = type {; } end : : = | : : = caste [] [Inheritances]; [] [] [] init ; body [; ] endcaste 24/3/2009 73

: : = observes { ; } : : = | all in | var in [: =] | set in [: =] : : = { ; } : : = var : [: = ] : : = { ; } : : = action [ ( )] [] : : = 24/3/2009 74

: : = | begin {; } end | | | | | | | | | : : = with do end : : = When { -> } End 24/3/2009 75

: : = join [( )] |quit |suspend |resume : : = create [ of] [( )] [@] | destroy [] 24/3/2009 76

7. Virtual Machine CAVM: Architecture Communication Engine Computer C 2 Computer C 1 CE 2 Computer Cn LEEk CE 1 Computer C 3 LEE 1 LEE 2 LEE 3 Local Execution Engine 24/3/2009 77

Structure of LEE network local Communication Manager Memory Space Agent Am context data Program Space Central Processing Unit (CPU) Context Registers List of Loaded Castes (LLC) … Loader PC Environment data List of Agents (Lo. A) 24/3/2009 Agent A 1 context data network object code OC 1 object code OC 2 … object code OCn 78

Structure of CE network local network Publication Space Agent A 1 s/a data … Agent Ai s/a data Membership List (of active Agents) 24/3/2009 Receiver Dispatcher Communication Manager Deployment Manager Membership Manager Caste object code 79

Deploy & Execute CAOPLE Programs CAOPLE Source Code Compile CAVM Object Code Caste SC 1 Caste SC n Caste OC 1 Caste OCn Deploy Computer C 2 Computer C 1 CE 2 Computer Cn Computer C 3 LEE 1 24/3/2009 LEEk LEE 2 80

Caste Deployment The deployment tool supports distributed deployment of object code through the internet. 24/3/2009 81

CAVM’s Messages A a LEE Register/Unregister CE observe b LEE a A update CE update b LEE A instanceset CE a a a 24/3/2009 82

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 24/3/2009 83

Examples of Instructions • • • … loadcaste 0 agentnew agentalloc dup storevar 3 agentreg sendmessage agentregpost agentready … 24/3/2009 # load a caste referenced by constant[0] # create new agent context # allocate agent. Info (memory space) # duplicate agentinfo at the stack top # store agent's variable to localvar[3] # prepare and push the register message # send the message # postprocess the response # mark agent context as ready 84

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 24/3/2009 85

Experiments with CAVM • 5 experiments – basic functions of LEE and CE – deployment and interaction – performance and scalability – centralized (localhost) vs. distributed (LAN) 24/3/2009 Peer-1: Monitor-1: caste Peer; action say. Hello() begin //do nothing end body say. Hello() endcaste Peer caste Monitor; observes all p in Peer; var num. Of. Peers: Integer; init num. Of. Peers : = 0; body when exist p in Peer: [say. Hello()] -> begin num. Of. Peers : = num. Of. Peers + 1; output( “Number of Peers: ”, num. Of. Peers); end endcaste Monitor Peer-2: caste Peer; observes all p in Peer; action say. Hello() begin //do nothing end action welcome(P: Agent. ID of Peer) begin //do nothing end body when exist p in Peer: [say. Hello()] -> if (p<>self) then welcome(p); endcaste Peer 86

Exp. 1 and 2 - basic • Peer-1 + Monitor-1 (Exp 1: centralized; Exp 2: distributed) – The performances of LEE in terms of IPm. S (Instruction per Milli-second) and MPm. S 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 24/3/2009 87

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 24/3/2009 88

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 24/3/2009 89

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. 24/3/2009 90

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 24/3/2009 91

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 24/3/2009 92

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. 24/3/2009 93

References For the list of publications on caste centric methodology, please see http: //cms. brookes. ac. uk/staff/Hong. Zhu/Publ ication. htm 24/3/2009 94