SMAC - IRIT – UPS Sylvain Rougemaille , TOULOUSE Jean-Paul Arcangeli,

1. ADELFE Design, AMAS-ML in Action A Case Study SMAC - IRIT – UPS Sylvain Rougemaille, TOULOUSE Jean-Paul Arcangeli, FRANCE Marie-Pierre Gleizes, Frédéric Migeon ESAW 08 1

2. Case Study: Foraging Ant • Simple but illustrative example • Already developed in our team [Topin 99] • Adaptive MAS approach adequacy • Behaviours entirely specified • Focus on modelling language and transformations • Environment: • Nest, Obstacles, Ants, Food, Pheromone • Goal: foraging ! ESAW 08

3. Results • Simulation tool • 3 man/day • Behavior rules • 0,5 man/day • Functional Details • Speed modulation • Food editing • Ants managing • Zooming ESAW 08

4. Outline • Problematics • Adaptive Multi-Agent Systems • Flexible Agentmodel • ADELFE Methodology • Model Driven Engineering • Model Driven ADELFE • Conclusion and Future Works ESAW 08

5. Problematics • Adaptive Multi-Agent Systems • Self-organising systems • Support system functional adaptation • Flexible Agentmodel • Agent operating mechanisms adaptation • Proposition: Combine AMAS and Flexible agent inthe design of complex systems • Aim: Benefit from both levels and bothconcerns ofadaptation ESAW 08

6. AMAS (Adaptive Multi-Agent Systems) • Principles : • Global function realized = result of the organizational process between agents • Change the organization: change the global function • To change the organization: self-organization by cooperation • Agents are in a cooperative state = functional adequacy is reached • Agents have to be cooperative • But there are unwanted situations: Non Cooperative Situations • No NCS detected nominalbehaviour is performed (local function) • NC state (exception or anticipation) cooperationfailure recovering ESAW 08

7. (Domain Specific) Modelling Language ESAW 08

8. Flexible Agent : ImplementationPrinciples • Modularity • Agent defined as micro-componentassembly • Re-usability • Micro-components constitute reusableunits • Mediator design pattern • The mediator gathers services from micro-components • Separation between: • Operating mechanisms • Agent behaviour • Delegation • Mediator delegates operating services to behaviour component ESAW 08

9. Combining Functional/Operational Adaptation • Self-adaptation of the system = cooperation of agents • Non Cooperative Situations detection • Implementation with flexibleagent • Agent oriented specificmiddleware different kinds of adaptation, different levels of concerns

10. Outline Problematics Adaptive Multi-Agent Systems Flexible Agentmodel ADELFE Methodology Model Driven Engineering Model Driven ADELFE Experiments Analysis Conclusion and Future Works

11. ADELFE Methodology • Main characteristics • Specific agent-based methodology Exploiting the AMAS Principles →cooperation Opensystems, adaptive to changes in the environment • For engineers aware of MAS • Principles • Based onRUP and standard notations (UML, AUML) • Topdown approach: Analysis phase - identification of agents • Bottomup approach: Design phase – agent design • Needs • Precise and specific concepts to assist the designer’s task • Specification of cooperationrules • Guidelines forthe system implementation ESAW 08

12. Model Driven Engineering • Aim: ease systems design • Promote models as “first class citizen” • Models provide abstraction • Models define precise concepts for systems design • Models are conform to meta-models (defined with MOF (OMG), Ecore (Eclipse)) • Automatic treatments • Means to assist designers and developers • Gather and automate goodpractices or expertise • Support by modeltransformations (transformation languages: ATL†, Kermeta‡) • Allow code generation • Domain Specific Modelling Language • Dedicated modelling language (concise and specific) • Described by a domain meta-model (close to domain experts needs) († http://www.eclipse.org/m2m/atl/) (‡ http://www.kermeta.org/) ESAW 08

13. Outline Problematics Adaptive Multi-Agent Systems Flexible Agentmodel ADELFE Methodology Model Driven Engineering Model Driven ADELFE Domain Specific Modelling Language Design Implementation Experiments Analysis Conclusion and Future Works ESAW 08

14. Domain Specific Modelling Language • AMAS-ML : Adaptive Multi-Agent System Modelling Language • Dedicated to the specification of : • System composition (agents, entity) • Agent Cooperative properties • Agent Cooperative behaviour • µADL : micro-Architecture Description Language • Dedicated to the specification of : • Specific agent middleware • Agent operating mechanisms models ESAW 08

15. Model Driven ADELFE (1/2)Design • EnhancedDesignPhase • Use of UML 2.0 • Use of AMAS-MLdiagrams to specify : • System / environment composition • Cooperative agent structure • Cooperative agent behaviour: • Cooperation and nominal rules • Use of modeltransformations : • Link AMAS-ML to UML2.0 : • Get information from requirements model • Express interactions thanks to UML Sequencediagrams ESAW 08

16. Model Driven ADELFE (2/2)Implementation • Implementation phase • Wanted result: AMAS Implementation using flexible agent middleware capabilities. • Need: to express concernsseparation (operational/behavioural) between AMAS concepts. • Modeltransformations are used to: • Automate the mapping between AMAS-ML and µADL. • Generate agent behaviour code. • Make Agent Yourself (MAY) generation tool: • Generate specific flexibleagentmiddleware • Use µADL model as input ESAW 08

17. Transformations Overview 2. AMAS-ML to Java : ATL Transformation 2 queries, 10 helpers, 130 code lines. Example : -- Transforming AMAS Actuator into homonymic muADLMuComponents. helper contextAMAS!Rule def : generateIfThenElse(): String = '\t/**\n\t* Generated '+ if self.oclIsTypeOf(AMAS!CooperativeRule) then 'cooperative rule : ' +self.name+' handles '+self.handledNCSName()+ ' situation :\n\t* ' +self.descriptionelse 'standard rule : '+ self.name endif +' \n\t*/\n' +'\tif ('+ self.trigger.condition.generateCondition()+'){\n' +self.impliedActions->iterate(a; accA: String=''|accA+'\t\t'+a.generateAction()+'\n\t\t}'); 1. AMAS-ML to µADL : ATL Transformation 12 rules, 5 helpers, 380 code lines. Example : -- Transforming AMAS Actuator into homonymic muADLMuComponents. ruleActuators2MuComponent{ fromactuator : AMAS!Actuatorto actuatorCt:muADL!MuComponent( name <- actuator.name, provided <- thisModule.resolveTemp(actuator,'providedActuatorInterface'), privateServices <- actuator.actions->collect(act|thisModule.resolveTemp(act,'service')) ), providedActuatorInterface:muADL!Interface( name <- actuator.name+'I' ) } ESAW 08

18. Developer Conclusion • Simple, Efficient, Automated • Prototype in 3 days, Behaviour part 0,5 day • Ant API, 53ko, 17 classes, 9 interfaces • Environment, 69ko, 29 classes • Behaviour and main, 6ko, 2 classes • API Details • Kernel : 4 classes, 1 “markup” interface Agent • Generated micro-components : 1 class per each ESAW 08

19. AMAS Designer Conclusion • New version of ADELFE : • Using model driven approach: • Specific languages (AMAS-ML, µADL) • Model transformations • Automations in the development process : • Facilitate phases transition (from analysis to design) • Allow to bridge generic (UML) and specific (AMAS-ML) modelling • Ease the implementation • Developers focus on application dependent concerns ESAW 08

20. Future Works • Improve behavioural design • AMAS-ML typesystem to specify instancevalues • Investigate templatebasedlanguage to generate code • Provide a fullyintegrated tool including : • An assistant guiding users all along the process • Model validations and simulation • Provide an adaptivemethodologicalframework • Assist users by proposing adequatemethodfragments ESAW 08

21. Thank you for your attention Questions? ESAW 08

22. Elsy KaddoumMASC Opérateurs Conteneurs Trois types d’agents coopératifs • Conteneur • Opérateur • Station ESAW 08

23. Elsy KaddoumMASC ESAW 08