A Domain Specific Metamodel for Semantic Web ...

A Domain Specific Metamodel for Semantic Web enabled Multi-Agent Multi Agent Systems Moharram Challenger, Sinem Getir, Sebla Demirkol and Geylani Kardas

This work is supported by The Scientific and Technological Research Council of Turkey (TUBITAK) under grant 109E125. 1

C t t Contents • Introduction: MAS, SW, MDD • Background • Metamodel in Viewpoints p – – – – – –

SWA’s Internal VP Protocol VP MAS & Org. VP Role & Behaviour VP Environmental & Services VP Agent-SWS Interaction VP

• Graphical Concrete syntax – Case study: e-barter

• Conclusion and future works 2

Introduction - MAS • Software agents: Autonomous software components capable of acting on behalf of their human users in order to perform a group off defined d fi d tasks k • MAS (Multi Agent System): Systems in which many intelligent software agents interact with each other, either cooperative or selfish [Sycara, 1998] • AOSE (Agent (A t Oriented O i t d Software S ft Engineering) E i i ) deals d l with: ith – ACL (Agent Communication Lang) – Interaction Protocols – Architectures: e.g. BDI, Practical Reasoning, Deductive reasoning, Reactive agents – Development methodology: e.g. Tropos, Gaia, INGENIAS – Platforms: JADE, JADEX, SEAGENT, JACK

3

Introduction – Semantic Web • Semantic Web: allows machines to understand the meaning or semantics of info on the Web

• Th The agents t would ld be b able bl to t perform f tasks t k automatically t ti ll andd locate related information on behalf of the user • Semantic web uses: – RDF, RDFS, OWL: Web ontology Language

• Semantic web can create a distributed related data (traversable) • SW let web-pages to be interpreted with ontologies • This Interpretation can be done via Agents

4

I t d ti – MDD & DSML Introduction • • • • • •

MASs Ss aree complex, co p e , even eve more o e complex co p e with w Semantic Se c Web Need more abstraction and a Metamodel can be a solution MDD changes focus of development from code to model Metamodel for MASs provides platform independent modeling. Final goal of this ongoing study is to provide a DSML DSML allows end-users & domain experts to specify a problem usingg visual abstractions close to domain – In fact, DSMLs can be considered as a graphical type of the traditional DSL

• We will call this DSML as SEA-ML (Semantic Enabled Agents –Modeling Language) – With Abstract syntax, Concrete syntax, Formal Semantics and required tools for code generation 5

R l t d works Related k – MAS meta-modeling t d li • ACSM (Agent Class Superstructure Meta-model): Provided by IEEE FIPA’s Technical Committee and OMG. – Specially its internal agent behaviours and agent communications are very concise – Defines 8 entities & their relations but it is too abstract & needs extensions

• Bernon et al. [8] give a metamodel for MAS development – They merge the most significant contributions of some methodologies: ADELFE, Gaia and PASSI

• Molesini et al. al [9] give a metamodel for SODA methodology – It aims to model Interaction and Social aspects of SODA

6

R l t d works Related k – MAS meta-modeling t d li • Both Bernon et al and Molesini et al’s studies: – Consider a specific methodology, but not general MAS modeling

• FAML Metamodel [10] is a synthesis of existing metamodels – It is a generic MAS metamodel – It has Run-time, Design time, Agent internal and external aspects

• Hahn et al [11] give a PIMM in various viewpoints – It I iis a generic i MAS metamodel d l

• Neither FAML nor Hahn’s study – Support Agents on the Semantic Web – Support the Interaction of SWAs with their Environment

7

R l t d works Related k • In the study of [Kardas et al., 2009], a model driven software development process has been introduced for semantic web enabled MASs. • Kardas et al introduced a Platform Independent Meta Model (PIMM) for Semantic Web enabled Multi Agent Systems. • They Th have h used: d – FIPA ACSM – UML components

• But the study – Needs updating according to new specifications – Does not offer Concrete syntax and Semantics – Does not offer a Toolkit for code generation and Validation 8

N Ab New Abstract t tS Syntax t • PIMM iintroduced t d d in i the th study t d off [Kardas [K d ett al., l 2007] is i a base b meta-model of semantic web enabled MAS • Abstract Syntax defines meta-elements meta elements and their relationships • Our meta-model will consider various aspects of MAS development: – Behavioral , Organizational, Protocol, Structural and …

• And pprovides both – Internal modeling of a software agent, and – Interaction of agents and semantic web services

• First, we revised the PIMM of Kardas et al. • Second, due to its complexity, we divided it into 6 Viewpoints 9

Ab t t Syntax Abstract S t – Meta-Model M t M d l • Revision: – – – –

Omitting out of date FIPA elements: Group, Classifier and … Deleting useless UML elements: Behavioral Feature Feature, UML Profiles Plugging OMG’s recent ODM (Ontology Definition Metamodel) Adding g BDI agent’s g internal meta-elements

• Division: 1. 2. 3. 4. 5. 6 6.

SW Agent’s Internal viewpoint Protocol Viewpoint MAS & Organizational Viewpoint Role l & Behavior h i Viewpoint i i Environment & Services Viewpoint Agent-Semantic Web Service (SWS) Interaction Viewpoint 10

1 Semantic 1. S ti Web W b Agent’s A t’ Internal I t l Viewpoint Vi i t • In a “Semantic Se c Web enabled e b ed MAS”, S , software so w e agents ge s can: c : – Collect Web content from different sources – Process the info – Exchange the results

• Also they can – Evaluate semantic data – Collaborate with semantically defined entities such as semantic Web Services using Content Languages

• Agents with these capabilities are Semantic Web Agents • A Semantic Web Agent can interact with both – Other agents and – Semantic web services 11

1 Semantic 1. S ti Web W b Agent’s A t’ Internal I t l Viewpoint Vi i t • • • •

A SWA can play several Roles and can change them over time SWA can have various states but onlyy one Agent g Type. yp This VP models SWA’s Internal Structure pp BDI Architecture ((as well as reactive one)) It supports – – – –

Beliefs as info about environment, composed of Facts Goals to be achieve Plans to do Behaviors (the tasks in fact) to achieve goals Capabilities: including Beliefs, Goals and Plans

12

1. Semantic Web Agent’s Internal Viewpoint

13

2 Protocol 2. P t l Viewpoint Vi i t • Represents interactions and communication elements • It has elements and relations such as: – Protocol, Interaction, Message, and Message Type

• The Interactions are FIPA Compatible • Each Interaction has several Messages triggered by Behaviors • An agent in different Roles realizes different Protocols • Message Type can be (as FIPA ACL): – Inform, Request, CFP, Ack, Accept, Refuse and …

14

2 Protocol Viewpoint 2.

15

3 MAS and 3. dO Organizational i ti l Vi Viewpoint i t • D Deals l with ith the th Construction C t ti off a MAS as a whole h l • Includes main blocks which compose the complex system as an organization • SW Organization is composed of SWAs • An agent can reside in more than one Organization • A SW Organization can be composed of several sub-org’s • A SW sho should ld have ha e one or more Ontologies special for the Org. to: – Gather Information – Do Reasoning for MAS members

• Collection of Ontologies g makes Knowledge-Base g of MAS 16

3 MAS and 3. dO Organizational i ti l Vi Viewpoint i t

17

4. Role and Behavior Viewpoint • Deals with complex controlling structure of the Agents • Agent’s Roles and Behaviors are modeled in this viewpoint • Role is a base element for : – Domain, Domain Architecture, Architecture Registration and Ontology Mediator Roles

• A Role should have one or more Behaviors and can participates in Scenarios • A behavior consists of one or more Tasks which are composed p of Actions • A SWA can advertise several services • Association between SWAs and SWSs is pprovided by y Role entities e.g. Registration Role • The SWAs p playy roles which use ontologies g to – Maintain their internal knowledge and Infer about the environment 18

4. Role and Behavior Viewpoint

19

5 Environmental 5. E i t l andd Services S i Viewpoint Vi i t • Resource access and use are considered in this viewpoint – E.g.: Ontologies, Knowledge-bases, Discovery and execution services

• IInteraction t ti off agentt with ith resources in i the th environment i t are modeled • An Agent can access many Resources • An Environment can include many Resources and SWS • An A E Environment i t can have h a Permission P i i Table T bl for f access control • A Role can accesses to an Environment • A SWS is composed of one or more Services (or Web ~) • Service Ontology is used by Semantic Web Services 20

5. Environmental and Services Viewpoint

21

6 Agent-Semantic 6. A tS ti Web W b Interaction I t ti Viewpoint Vi i t • Models the Interaction of SWAs with SWSs (an important one) • Each Service is described by 3 semantic documents: – Service Interface: representation of the service – Process Model: internal structure & execution dynamics of service – Physical Grounding: invocation protocol of the service

• In new metamodel, SWS components are: – Interface, Process, and Grounding

• And they can use – Input, Output, Precondition, and Effect entities

• Semantic Web Service: Is any service whose capacities and interactions are described semantically • Semantic S ti Web W b Service S i steps: t Discovery, Di Negotiation, N ti ti andd Execution E ti 22

6. Agent-Semantic Web Interaction Viewpoint • Types of plan in new meta-model: – Semantic Service Finder Plan: Discovery of candidate semantic web service – Semantic Service Agreement Plan: 1- negotiation on QoS metrics of the service (e.g. service execution cost, running time, location) 2- Agreement settlement – Semantic Service Executor Plan: Executing suitable semantic web service

• Agents ge s need eed too communicate co u ca e with w a Service Se v ce Registry eg s y too discover d scove service capability. This duty is for a specialized agent called: Semantic Service Matchmaker Agent • Matchmaker agent can: – Store the capability advertisements of semantic web services and – Match capabilities with service requirements of agents 23

6. Agent-Semantic Web Interaction Viewpoint

24

C Concrete t Syntax S t • Concrete Syntax: Set of notations to facilitates the presentation & construction of a modeling language • Concrete syntax can be formulated in a textual or visual • Concrete syntax: maps meta-elements and their textual/graphical representations • We developed an Eclipse GMF based graphical modeling editor to support the proposed concrete synatx of SEA_ML

25

G hi l Concrete Graphical C t Syntax S t • • • •

First, Viewpoints are represented in KM3 Second, KM3 is converted to Ecore Third, graphical notation for each concept is chosen Finally, Eclipse GMF tools are developed based on Ecore models and using graphical notations. – Some basic constrains are considered

• GMF tools can validate instance model based on metamodel

26

Concepts and notations of graphical concrete syntax

27

C t i t in Constraints i Concrete C t Syntax S t Level L l • Compartment constraint: – Aggregation relation in Ecore is converted to Compartment – To have nested elements, they should have Aggregation relation – E.g.: Belief and Fact are nested, but Role and SWA cannot be nested

• Number N b off R Relation: l i – According to one-to-one, one-to-many or many-to-many relations in Ecore number of the relations between elements are controlled. Ecore, controlled – E.g.: SWA can play several Roles, but it can have only one Agent Type

• Relation source & destination: – Direction defined in Ecore is used to limit the connection source and destination – E.g.: E Plan Pl to Goal G l is i permitted i d while hil vice i versa is i not permitted i d 28

C t i t in Constraints i Concrete C t Syntax S t Level L l • Inheritance relation: – Defined Inheritance in Ecore forces some constrains in instance model – A subclass will include all of the attributes and relations of superclass – E.g.: E g : Plan and Semantic Service Finder Plan

• Integrity of relations: – When an element is removed, removed all of the relations starting or ending to the element are omitted automatically.

29

C Case Study: St d e-barter b t • B Barter t system: t a commerce approachh where h customers t meett att a marketplace in order to exchange their goods or services without currency • Agent-based e-barter system consists of agents that exchange ggoods or services on behalf of owners corresponding p g to their preferences • In this application, base scenario is achieved by – Customer agent: Responsible for adding & evaluating barter proposal – Barter Manager agent: Manages all trades in the system (Collecting and Matching proposals as well as tracking the bargaining) – Cargo agent: Transporting barter product

30

Case Study: e-barter • A GENERAL instance model for the e-barter system [Geylani K ,2010] K. 2010]

31

B t Manager’s Barter M ’ Internal I t l Structure St t • Barter Manager Agent is a SWA & has a role named Barter Role • It also can have its states and a type • It has Capabilities (Goals, Beliefs and Plans) (BDI Structure) • Knowledge of Barter manager is kept in Beliefs which are composed of facts • It has also Plan library to behave properly in different situations • It applies two plans called discoverBarterService and invokeBarterService for interaction with a SWS 32

Case-Study: E-barter - SWA Internal Viewpoint

33

B t Manager Barter M SWS IInteraction t ti • Barter Manager may need to interact with SWS to match bidden and demanded goods • SWS here is Barter Service – It can be used to infer about semantic closeness between items [22]

Barter Manager: • First, needs to find the service: Discover barter service plan – Which also needs service interface (here Barter Service Interface)

• Then, the negotiation: Interaction with candidate servise • Finally, Execution of the service: Invoke Barter Service plan – Which also needs Process element (Barter Service Process) and uses Grounding 34

Case study: E-barter – SWS Interaction Viewpoint

35

C l i andd future Conclusion f t workk • In this study, study a meta-model is provided for semantic web enabled MASs • The base PIMM is refined and divided into several viewpoints • Graphical p Concrete Syntax y is pprepared p in Eclipse p GMF considering some basic constrains • The required tool is developed for validation of instance models • A case study is provided to use the metamodel (e-barter) • We will provide semantics and code generation for a DSML based on this metamodel 36

References [1] Russell, S. J., Norvig, P., 2003. Artificial Intelligence: A Modern Approach, second ed., Pearson Education, USA, 1080p. [2] Sycara, K., 1998. Multiagent Systems. AI Magazine. 19 (4), 79-92. [3] Berners Berners-Lee, Lee T., T Hendler, Hendler J., J Lassila, Lassila O., O 2001. 2001 The Semantic Web. Web Scientific American American. 284 (5), (5) 34-43. 34 43 [4] Kardas, G., Goknil, A., Dikenelli, O. and Topaloglu, N. Y. (2009) "Model Driven Development of Semantic Web Enabled Multi-agent Systems", International Journal of Cooperative Information Systems, World Scientific Publishing Company, Vol. 18, Issue 2. [5] Schmidt D. Model-Driven Engineering. Guest Editor's Introduction. IEEE Computer, Feb 2006. [6] Gray J., Tolvanen J-P., Kelly S., Gokhale A., Neema S., and Sprinkle J. “Domain-Specific Modeling,” Handbook of Dynamic System Modeling, (Paul Fishwick, ed.), CRC Press, ISBN: 1584885653, 2007. [7] Harel D. and Rumpe B. Meaningful modeling: What’s What s the semantics of semantics? IEEE Computer, 37(10), 2004. [8] Chen K., Sztipanovits J., Neema S. Toward a semantic anchoring infrastructure for domain-specific modeling languages. EMSOFT 2005: 35-43. [9] Di Ruscio R i D., D Jouault J lt F., F Kurtev K t I., I Bezivin B i i J., J Pierantonio Pi t i A. A Extending E t di AMMA for f supporting ti dynamic d i semantics specifications of DSLs. Technical Report HAL -CCSd - CNRS, Laboratoire D'Informatique de NantesAtlantique, 2006. [10] Chen K., Sztipanovits J., Neema S. Compositional specification of behavioral semantics. Design, Automation and Test in Europe Conference and Exposition (DATE 2007), April 16-20, 2007, Nice, France, pp. 906-911. [11] Bellifemine, F., Poggi, A. and Rimassa, G., 2001, Developing Multi-agent Systems with a FIPA-compliant Agent Framework, Software Practice and Experience, 31: 103-128. [12] Dikenelli, O., Erdur, R.C., Kardas, G., Gümüs, O., Seylan, I., Gurcan, O., Tiryaki, A.M. and Ekinci, E.E., 2006, Developing Multi Agent Systems on Semantic Web Environment using SEAGENT Platform, Lecture Notes in Artificial Intelligence, 3963: 1-13. 37

Thanks & Any Questions ?

38