A MDA Approach Using MAS-ML 2.0 and JAMDER

Transcription

1 A MDA Approach Using MAS-ML 2.0 and JAMDER Yrleyjânder S. Lopes, Enyo J. T. Gonçalves, Mariela I. Cortés, Emmanuel S. S. Freire Universidade Estadual do Ceará, Av. Paranjana 1700 Itaperi, Fortaleza CE, Abstract. Agent-based Software Engineering aims to develop methods, techniques and tools to enable the development of systems focused in agents to satisfy its specification. Overall, in software development context, the coding phase aims at coding the system in consistency with the detailed project developed with a group of designed models. As a result, the synchronization between the involved tools generating the artifacts of modelling and implementation, is very important to reduce the existing semantic gap between different levels of abstraction. In this context, this paper proposes an approach for generating code in Multi Agent System from templates in order to increase the productivity and the standardization of the generated code, ensure the consistency between documentation and code, and the traceability between the artifacts. Keywords: Multi-Agent System, Model Driven Architecture, MAS-ML, JADE 1 Introduction An agent is an entity capable of perceiving its environment through sensors and acting on that environment through actuators [1] and, depending on its internal architecture, to obtain knowledge, to keep the history of actions, among other features. The term MAS (Multi-Agent System) refers to the subfield of artificial intelligence which investigates the behaviour of a set of autonomous agents, aiming at solving a problem that is beyond the capacity of a single agent [2]. MAS involves a rich variety of entities such as organizations, environments, agents and object roles, that each one has relationships and associated behaviours. In particular, a single MAS can be composed of agents with different architectures. The utilization of methods and tools to support engineering activities allows an increase in productivity and ensures the correctness of the generated artifacts (documents and code). In general, the existence of a mapping between concepts and entities used in the different abstraction levels (modelling and implementation) facilitates the construction process of the computational solution to the problem. The definition of a mapping aims to establish how the proposed entities in the modelling phase can be implemented in a programming language. As a benefit, it is expected to increase the developer productivity, minimizing the possibility of inconsistencies between design and code. In this context, this paper presents an approach based on model-driven architecture (MDA) to support the code generation in JAMDER from MAS-ML 2.0 modelling language. This paper is organized as follows: Section 2 presents the concepts of MAS-ML 2.0, JAMDER and MDA, Section 3 presents some related work. Next, in Section 4, presents adfa, p. 1, Springer-Verlag Berlin Heidelberg 2011

2 the creation of Acceleo templates for JAMDER. In Section 5, a case study applying these templates for a MAS and, finally, conclusions and future works are described in Section 6. 2 Background MAS-ML 2.0 [3] language and JAMDER [4] framework, through JADE, provide the mechanisms needed to support some MAS development. These tools provide support for two different stages of the development process. MAS-ML 2.0 language focuses on the analysis and design phases, while JAMDER framework, on the coding phase. These two tools can be used in the context of the MDA architecture for code generation from MAS-ML 2.0 models to JAMDER code. The following subsections present detailed characteristics of each these tools. 2.1 MDA The model-driven architecture (MDA) is presented as an appropriate approach to assist in the generation of code because the code can be generated several times without compromising the model [5]. The OMG 1 [6] defines some standardizations in this process that are not necessarily associated to a specific platform, thus, the concepts may be applied to different modelling and implementation languages. The transformation process takes place through the steps proposed by OMG. The generated artifacts in each of these steps are independent of a specific tool for a better use, for example, when the same application can be implemented in different languages. The concepts of each of these steps are described as follows [6]: CIM (Computation Independent Model): rules for functional requirements; PIM (Platform Independent Model): relations between properties and their entity relationships. Ex.: models in MAS-ML Tool; PSM (Platform Specific Model): definition of how the system will work on a specific platform. Ex.: Java; PDM (Platform Definition Model): definition of how the PIM to PSM will work. Ex.: Plugin Acceleo; 2.2 MAS-ML 2.0 and MAS-ML Tool MAS-ML 2.0 [3] is a modelling language extension of MAS-ML [7] in order to support the modelling of the several agent architectures and their behavioural characteristics: Simple Reflex Agent: Perception and Action (based on Condition-Action Rules); Model-based Reflex Agent: Perception, Next Function and Action (oriented by Condition-Action Rules); Goal-based Agent with Plan: Plan and Action (based on selected plan according to Goal); Goal-based Agent with Searching: Perception, Next Function, Formulate Goal Function, Formulate Problem Function, Planning and Action; 1 Object Management Group,

3 Utility-based Agent: Perception, Next Function, Formulate Goal Function, Formulate Problem Function, Planning, Action and Utility Function. Depending on the agent's internal architecture, some of these properties are not required, for example, the simple reflex agent does not have beliefs and goals. Additionally, MAS-ML 2.0 also contains other entities that are typically found in a MAS: Object: any class that is used by other entity; Object Role: guides and restricts the behaviour of an object; Agent Role: are defined in the organization (owner) and exercised by an agent or sub-organization (player). They have rights and duties, which tells the agent or sub-organization what they can or should do, respectively; Organization: grouping of agents and they are included in one environment. It can contain agents or sub-organizations, that the work as agents, exercising agent roles; Environment: where the agents, organizations and objects inhabit; The MAS-ML Tool supports the modelling of MAS-ML 2.0 diagrams (organization diagram, role diagram and class diagram), on the basis of the language metamodel, which consists in a plugin for eclipse [8]. 2.3 JAMDER Definition JAMDER - JADE to MAS-ML 2.0 Development Resource [4] is an extension of the JADE framework that incorporates some classes in order to implement specific information present from the modelling attributes and methods. JAMDER contemplates the mapping to identify which elements from the conceptual MAS-ML 2.0 metamodel for agents are related to JADE framework. So, three hierarchies of agents at modelling level are established in JAMDER, one for goal-based agent with plan (from MAS-ML), the other for reflex agents (from MAS-ML 2.0) and one for cognitive agents (from MAS-ML 2.0). The JAMDER agent classes (Fig. 1) defined by Lopes [4] are described below: GenericAgent: This class inherits from jade.core.agent, and was defined to contain the properties and other common attributes between the three branches (Fig. 1). The common attributes are: (i) the list of agent roles, (ii) the list of organizations which the agent can participate, (iii) the environment which it is located and (iv) the list of actions it can perform. ReflexAgent: Representation of simple reflex agent, includes condition-action rules to select actions according to the current perception; ModelAgent: It represents the model-based reflex agent. It is branded by storing the beliefs that are used to decide the next action through next-function(belief) method. Beliefs cannot be accessed by other agents; GoalAgent: It represents the goal-based agent with planning and generates a plan with actions by runtime. It uses other methods: formulategoalfunction(belief) to generate a goal, formulateproblemfunction(belief, goal) to generate the list of actions and planning(actions) to put this action list in a order to be executed; UtilityAgent: It represents the utility-based agent that has the same structure of goal-based agent, but it has new method, utilityfunction(action), in order to guide the agent behaviour to identify the priority of the actions when there are more than one goal to be achieved;

4 MASMLAgent: It represents the goal-based with plan agent. The plan is created in modelling time. Fig. 1. JAMDER agent hierarchy. JAMDER follows the hierarchy defined in MAS-ML 2.0, which assigns a specific role of agent of agent types: AgentRole is related to ReflexAgent, ModelAgentRole is related to ModelAgent and ProactiveAgentRole related to GoalAgent, UtilityAgent and MASMLAgent. An organization is defined in JAMDER through the Organization class and defines a JADE container where agent roles and object roles are defined. The environment represents the JADE platform and defines methods for managing (adding and removing) other entities. This entity is represented by the Environment class in JAMDER. Its constructor has three parameters, the name that identifies the environment, the host and port that informs the address and port for this JADE platform, respectively. The object and object role are represented by the classes, Object and Object Role in JAMDER, respectively. 3 Related Work The agent-based development paradigm requires some appropriate techniques to explore its benefits and its own characteristics, supporting the production and maintenance of software [9]. Santos [10] provides a metamodel that represents the concepts that define an agent system, as well as the relationships among them. The code generation proposed by Santos [10] uses a prototype tool developed in Velocity [11] and the modelling information is represented by XML (Extensible Markup Language) file that contains the agent structures. The author uses a prototype of a modelling tool, called MAS Modeler [10], where the textual information is filled through step-by-step screens. After that, this structure is stored in a XML file and can be used by the Velocity template for code generation in Semanticore framework [12], but only for agents. The absence of a modelling tool that enables the graphical design of the MAS model was also considered as a disadvantage.

5 De Maria [13] proposes the code generation based on MDA architecture for MAS using the MAS-ML modelling language and the framework ASF (Agent Society Framework) [13]. The Visual Agent tool is used for modelling and code generation. A major advantage is that the concepts used in MAS-ML modelling are found in the implementation proposed language, but not for all MAS-ML 2.0 agents. For code generation, it occurs in ASF from the MAS-ML artifacts, but there is a second transformation of this artifact, where it is transformed into a XMI (XML Metadata Interchange) file, and then be transformed into Java code (ASF). TAOM4E [14] is an agent oriented modeling environment and supports a modeldriven, an agent oriented software development. It has been designed taking into account MDA recommendations. TAOM4E architecture allows for a flexible integration of different tools. The tool is a plug-in for ECLIPSE Platform, however, it allows the code generation only to BDI (belief-desire-intention) agents. The Prometheus Design Tool (PDT) [15] is a graphical tool that is used to design a Multi-Agent System following the Prometheus Methodology. PDT is integrated into the Eclipse platform, enabling the users to accomplish the full development life-cycle of an agent-oriented application in one IDE and also inherit the rich set of product development features that Eclipse provides. Similarly to TAOM4E, the code generation is targeted only to BDI agents. 4 Code Generation As a tool to support the concept of MDA, there is another plugin for Eclipse that allows generating code in different implementation languages called Acceleo [16]. The plugin Acceleo [16] allows the incremental development, in other words, the code can be generated, modified and regenerated. MAS-ML Tool is used to create the diagrams. This tool generates the.masml file that stores the structure of data entities and structural and behavioural aspects defined in MAS-ML 2.0 as XMI format. To formalize the code generation in Acceleo, it is needed to establish a template for each entity through a language defined by the OMG, the MTL (Model Transformation Language) [6]. The Acceleo uses these templates that are stored in files as.mtl extension that can contain multiple templates for the generation. An Acceleo project in Eclipse is needed and it will contain the templates. When the template is executed, it will need the model (.masml) and the output folder that classes will be generated. Since five types of agents are modelled, each one is corresponding to a class that inherits from JAMDER. Each agent class in JAMDER is established according to the agent structure and behaviour that are modelled in the agent. Thus, the entities (agent, environment, agent role and organization) structure in the organization modelling diagram, MAS-ML Tool, are checked by Acceleo template. To differentiate the agents, the following sequence in below is checked: If the agent inherits from another agent, the inheritance relationship is held; If the agent does not define beliefs and goals, this agent inherits from ReflexAgent; If the agent defines beliefs, but do not have goals, this agent inherits from ModelAgent; If the agent defines a pre-defined plan, this agent inherits from MASMLAgent; If the agent defines a plan to set (planning) and do not have utilityfunction(), this agent inherits from GoalAgent;

6 If the agent defines a plan to set (planning) and have an utilityfunction(), this agent inherits from UtilityAgent; Regarding the object and object role, the generated classes inherit from Object (Java) and ObjectRole (JAMDER), respectively. The class for the organization entity inherits from Organization (JAMDER) and its constructor parameter defines the agent role. If different from null, the parameter indicates that the organization is a suborganization. Similarly to agents, the Acceleo template needs to identify which agent role type will be used on the inheritance when the agent role is created in JAMDER. The difference is established by the presence of components in the modelling of the role in the role diagram of MAS-ML. If the role does not have beliefs and goals, the agent role is an AgentRole type. If the role has not only beliefs, the agent role is a ModelAgentRole type. If the role contains the entire available structure, it is a type of ProactiveAgentRole. The environment is a class instance that inherits from Environment class (JAMDER). In addition to creating this class, it checks the relationships that the environment has to create instances of other entities in its constructor, as follows: organization, agent, agent role, object and role object. Relationships between entities are also checked, namely: organization and agent role, agent and agent role, organization and object role and object and object role. When the template Acceleo is executed, it checks all entity relationships (Table 1), and the link is created when necessary. The MAS-ML Tool diagram used for these entities and relationships will be organization diagram. Due to space constraints only the Environment class template is presented. Table 1. Acceleo template for Environment class. [template public generatejava(c : EnvironmentClass)]... public class [c.name/] extends Environment { public [c.name/](string name, String host,string port){ super(name, host, port); [for(i : Inhabit c.inhabit)] [if (i.org -> size() > 0) ] Organization [i.org.name/] = new Organization("[i.org.name/]", this, null, null); addorganization("[i.org.name/]", [i.org.name/]); [for(ow : Ownership i.org.ownership)] [for(ob : ObjectRoleClass ow.objectrole)] Object [ob.play.name/] = new Object(); ObjectRole [ob.name/] = new ObjectRole("[ob.name/]", [i.org.name/], [ob.play.name/]); addobjectrole("[ob.name/]", [ob.name/]); [/for] [/for] [/if] [/for]

7 [for(i : Inhabit c.inhabit)] [if (i.agentclass -> size() > 0) ] [let a : AgentClass = i.agentclass] GenericAgent [a.name/] = new [a.name/]("[a.name/]", this, null); AgentRole [a.play.agentrole.name/] = new AgentRole("[a.play. agentrole.name/]", [a.play.agentrole.ownership.owner.name/], [a.name/]); addagent("[a.name/]", [a.name/]); [/let] [/if] [/for] } } [/file] [/template] All templates for the entities are available on 5 Case study Moodle [17] is a Course Management System (CMS), also known as Learning Management System (LMS), which represents the interaction between students and teachers in a virtual environment. It became very popular among educators around of the world as a tool for creating dynamic websites for their students. The purpose of the case study is to illustrate the application of the proposed approach creating a multi-agent system to Moodle and identifying the agents through the modelling in MAS-ML Tool [3]. After that, the proposed code generation approach to JAMDER is illustrated. In order to execute the generation from.mtl file, it is necessary to use a project in Eclipse as Acceleo type. After that, it is needed to put the template code for the entities code in this file. Finally, when this file is executed it will require the modelling file (.masml) and the folder that will be used to store the generated classes. Based on Moodle, it is possible to define some entities (except object and object role for this study case) for the prototype of this project according to MAS-ML 2.0: Environment (MoodleEnv): there will be only one that will include the other entities; Organization (MoodleOrg): there will be only one too that will group the agents according to agent roles defined on it; Agents: the following agents were defined: CompanionAg (reflex agent): defined as model agent, it stores the students notes and sends messages for encouragement and affective interaction; PedagogicAg (goal-based with planning): it suggests courses and subjects that are related to what the student is doing; SearcherAg (goal-based with plan): it finds documents and people involved with the same theme of the student; HelperAg (reflex agent): it offers tips on how to make best use of a particular tool on Moodle.

8 GroupAg (utility agent): it suggests groups for agents according to the proposed theme or learning profile; Coordinator (goal-based with plan): it is responsible for ordering the actions of other agents, and mediating the conversation between them; Agent Roles: there will be only one agent role for each agent: CompanionAgRole, PedagogicAgRole, SearcherAgRole, HelperAgRole, GroupAgRole and CoordinatorAgRole; The next step in the development process is to execute the.mtl file that will check the entities and the relationships in the diagram.masml. According to the template for environment, it is responsible to create all instances for other entities in the MAS. So, the following code (Table 2) shows the entities instances created for this MAS according to the Moodle diagram (Fig. 2). Table 2. Environment class generated from Acceleo. import jamder.environment; import jamder.organization; import jamder.roles.agentrole; import jamder.agents.genericagent; public class MoodleEnv extends Environment { public MoodleEnv (String name, String host, String port) { super(name, host, port); Organization MoodleOrg = new Organization("MoodleOrg", this, null, null); addorganization("moodleorg", MoodleOrg); GenericAgent HelperAg = new HelperAg("HelperAg", this, null); AgentRole HelperAgRole = new AgentRole ("HelperAgRole", MoodelOrg, HelperAg); addagent("helperag", HelperAg); GenericAgent SearcherAg = new SearcherAg("SearcherAg", this, null); AgentRole SearcherAgRole = new AgentRole("SearcherAgRole", MoodelOrg, SearcherAg); addagent("searcherag", SearcherAg); GenericAgent CompanionAg = new CompanionAg( "CompanionAg", this, null); AgentRole CompanionAgRole = new AgentRole( "CompanionAgRole", MoodelOrg, CompanionAg); addagent("companionag", CompanionAg); GenericAgent CoordinatorAg = new CoordinatorAg ("CoordinatorAg", this, null); AgentRole CoordinatorAg = new AgentRole("CoordinatorAg", MoodelOrg, CoordinatorAg); addagent("coordinatorag", CoordinatorAg); GenericAgent GroupAg = new GroupAg("GroupAg", this, null); AgentRole GroupAgRole = new AgentRole("GroupAgRole", MoodelOrg, GroupAg);

9 addagent("groupag", GroupAg); GenericAgent PedagogicAg = new PedagogicAg("PedagogicAg", this, null); AgentRole PedagogicAgRole = new AgentRole("PedagogicAgRole", MoodelOrg, PedagogicAg); addagent("pedagogicag", PedagogicAg); } } Fig. 2. Moodle MAS modelling in MAS-ML Tool. To exemplify the generation for the agent entity, the Companion agent was used. This agent should be able to choose independently from a pre-established affective

10 interaction strategies range, such as support messages. It sends encouragement messages (positive reinforcement) when the user, through manifested interactions, gives evidence that does not present difficulties to follow the discussion, or the proposed tasks, or content, and even when the student has a rate average above of his class or work group. Due to the need to store the class notes for comparison and send messages quickly, this agent is featured as a reflex model agent. The interaction strategies and emotional messages are defined through the agent beliefs. The beliefs are contained and represented in a.pl file. The agent structure is defined in the Fig. 3: Fig. 3. Learning Companion Agent modelling This agent receives on his constructor the agent s name, instance for environment and the initial agent role. After that, the agent structure is created with the beliefs, actions and perceptions. Each action has preconditions and posconditions that check the status of the environment before and after the action execution. In this case, the actions do not have any condition. If the structure for ModelAgent has a nextfunction(belief, perception) and this name cannot be changed, then, the nextfunction- Companion(belief, perception) is called by nextfunction that checks if the action has not been executed and takes another action, according to belief and percept. The current version of MAS-ML Tool does not have the association between percept and action, so, this association must be done by developer after the code generation. The Table 3 shows the generated code for this agent. Table 3. The generated JAMDER class for CompanionAgent. import jamder.behavioural.*; import jamder.environment; import jamder.roles.agentrole; import jamder.structural.*; import java.util.list; import jamder.agents.*;

11 public class CompanionAg extends ModelAgent { public CompanionAg (String name,environment env, AgentRole agrole) { super(name, env, agrole); addbelief("beliefs.pl", new Belief("beliefs.pl", "String", "")); Action compareclassac = new Action("compareClassAc", null, null); addaction("compareclassac", compareclassac); Action displaysupportmessageac = new Action( "displaysupportmessageac", null, null); addaction("displaysupportmessageac", displaysupportmessageac); Action displayreinforcemessageac = new Action( "displayreinforcemessageac", null, null); addaction("displayreinforcemessageac",displayreinforcemessageac); Action askcoordinatoractionac = new Action("askCoordinatorActionAc", null, null); addaction("askcoordinatoractionac ", askcoordinatoractionac); addperceive("difficultydiscussions", null); addperceive("studentaccesscontent", null); } protected Belief nextfunction(belief belief, String perception) { return nextfunctioncompanion(belief, perception); } private Belief nextfunctioncompanion(belief belief, String perception) { return null; } } In this case study, one agent was illustrated to exemplify the modelling and its code generation in a MAS. 6 Conclusions The appropriate mapping between modelling and implementation becomes crucial for the system development to ensure the consistency and traceability between modelling and code artifacts. In this context, this paper aimed to develop a strategy for generating code from models, based on artifacts generated from existing MAS support tool. The strategy definition involves the properties and characteristics establishment in order to determine the modelling elements to be included in the code. The Acceleo was used to support the code generation by templates, which were developed in this paper, to create classes in JAMDER according to MAS-ML 2.0 modelling. One of the advantages of code generation is that it provides the transition from design phase to the implementation phase through the execution of the models proposed by model-driven architecture, reducing the time spent that it would take to map the information and generate the code manually. The union of these three tools (JAMDER, MAS-ML 2.0 and Acceleo) provides an easier development for MAS, taking advantage the use of Eclipse. After that, as JAMDER is based on JADE and it has an execution tool, the prototype project can be run on it. MDA helped to separate the usage of each tool used above, proving the change for tool as necessary.

12 Improvements in the process generation depend on the modelling of new features in the MAS-ML Tool. In this sense, the modelling of the combination of actions to the plan pre-defined is missing. In other hand, the inclusion of AI techniques in the generated code according to textual notes representing the logic approach to solve, for example, a neural network or fuzzy logic. As future work, code generation from MAS-ML 2.0 to other implementation languages is suggested, on the basis of the proposed approach. References 1. Russell, S. Norvig, P. Inteligência Artificial: uma abordagem moderna. 2 Ed. São Paulo: Prentice-Hall. p (2004) 2. Jennings, N. R. Agent-Oriented Software Engineering, in F. J. Garijo & M. Boman, eds, Proceedings of the 9th European Workshop on Modelling Autonomous Agents in a multiagent World: Multi-Agent System Engineering (MAAMAW-99), Vol. 1647, Springer- Verlag: Heidelberg, Germany, pp (1999) 3. Gonçalves, E. J. T., Farias, K., Cortes, M. I., Feijo, A. R., Oliveira, F. R., Silva, V. T. MAS-ML TOOL: A Modeling Environment For Multi-Agent Systems. ICEIS. (2011) 4. Lopes Y. S.; Gonçalves, E. J. T.; Cortés, M. I. e Freire, E. S. S. Extending JADE Framework to Support Different Internal Architectures of Agents. 9th European Workshop on Multi-agent Systems (EUMAS 2011), Maastricht, Netherlands (2011) 5. Souza, G. P. Modelagem de Sistemas Distribuídos usando MDA. Available in: TF09_2_RelatorioGiselePSouza.pdf, accessed on: December, 03 (2011) 6. OMG. Object Management Group Silva, V. T.; Choren, R.; Lucena, C. J. P. de (2007). MAS-ML: A Multi-Agent System Modeling Language. Conference on Object Oriented Programming Systems Languages and Applications (OOPSLA); Anaheim, CA. (2007) 8. ECLIPSE, Eclipse Platform Zambonelli, F.; Jennings, N.; Wooldridge, M. Organizational abstractions for the analysis and design of multi-agent systems. In: Ciancarini, P.; Wooldridge, M. (Eds.) Agent- Oriented Software Engineering, LNCS 1957, Berlin: Springer, p. 127 (2001) 10. Santos, D. R.; Ribeiro, M. B.; Bastos, R. M. A Comparative Study of Multi-Agent Systems Development Methodologies. In: XX Simpósio Brasileiro de Engenharia de Software (SBES 2006). Florianópolis. p (2006) 11. Velocity. Apache Velocity Project Blois, M., Lucena, C. Multi-agents Systems And The Semantic Web - The SemanticCore Agent-Based Abstraction Layer. In: ICEIS, Porto. (2004) 13. De Maria, B., Silva, V. T., Lucena, C. J. P. Usando MDA no Desenvolvimento de Sistemas Multi-Agentes. Rio de Janeiro (2004) 14. TAOM4E. Tool for Agent Oriented Modeling Padgham, Lin and Winikoff, Michael. Developing Intelligent Agent Systems: A Practical Guide. ISBN , John Wiley and Sons. (2004) 16. Acceleo. Acceleo OpenSource MOODLE, Moodle.