1. Deploying a Distributed Symposium Planner Through Rule Responder Benjamin Craig Harold Boley Institute for Information Technology National Research Council, Canada Fredericton, NB, Canada RuleML-2008 Challenge Orlando Florida October 30-31, 2008

2. 1 Outline Rule Responder Overview Rule Responder Overview Symposium Planner Use Case Symposium Planner Use Case Agents Agents Personal / Organizational / External Personal / Organizational / External Rule Engines (for Realizing Agents) Rule Engines (for Realizing Agents) Prova Prova OO jDREW OO jDREW Communication Middleware (for Connecting Agents) Communication Middleware (for Connecting Agents) Mule ESB Mule ESB Reaction RuleML Messages Reaction RuleML Messages Online Demo Online Demo Conclusion Conclusion

3. 2 Overview of Rule Responder Rule Responder is an experimental multi-agent system for collaborative teams and virtual communities on the Web Rule Responder is an experimental multi-agent system for collaborative teams and virtual communities on the Web Supports rule-based collaboration between the distributed members of such a virtual organization Supports rule-based collaboration between the distributed members of such a virtual organization Members of each virtual organization are assisted by semi-automated rule-based agents, which use rules to describe the decision and behavioral logic Members of each virtual organization are assisted by semi-automated rule-based agents, which use rules to describe the decision and behavioral logic Implemented on top of a Mule-based Service Oriented Architecture (SOA) Implemented on top of a Mule-based Service Oriented Architecture (SOA)

4. 3 Use Case: Symposium Planner RuleML-20xy Symposia RuleML-20xy Symposia An organizational agent acts as the single point of entry to assist with the symposium: An organizational agent acts as the single point of entry to assist with the symposium: Currently, query answering about the symposium Currently, query answering about the symposium Ultimately, preparing and running the symposium Ultimately, preparing and running the symposium Personal agents have supported symposium chairs since 2007 (deployed as Q&A in 2008) Personal agents have supported symposium chairs since 2007 (deployed as Q&A in 2008)Q&A General Chair, Program Chair, Panel Chair, Publicity Chair, etc. General Chair, Program Chair, Panel Chair, Publicity Chair, etc.

5. 4 Organizational Agents The organizational agent represents the goals and strategies shared by each committee chair The organizational agent represents the goals and strategies shared by each committee chair It contains rule sets that describe the policies and regulations of the RuleML Symposium It contains rule sets that describe the policies and regulations of the RuleML Symposium Delegates incoming queries to the chair’s PAs Delegates incoming queries to the chair’s PAs

6. 5 Personal Agents A personal agent assists a single chair of the symposium, (semi-autonomously) acting on his/her behalf A personal agent assists a single chair of the symposium, (semi-autonomously) acting on his/her behalf Each personal agent contains a rule-base FOAF-like profile Each personal agent contains a rule-base FOAF-like profile It contains a FOAF*-like fact profile plus FOAF-extending rules to encode selected knowledge of its human owner It contains a FOAF*-like fact profile plus FOAF-extending rules to encode selected knowledge of its human owner * The Friend of a Friend (FOAF) project: http://www.foaf-project.orghttp://www.foaf-project.org

7. 6 External Agents External agents exchange messages with the RuleML-2008 OA. External agents exchange messages with the RuleML-2008 OA. They submit queries and receive answers They submit queries and receive answers End users, as external agents, interact with the OA using a Web (HTTP) interface to the Symposium Planner End users, as external agents, interact with the OA using a Web (HTTP) interface to the Symposium Planner Support for simultaneous external agents Support for simultaneous external agents Many EAs can communicate with the OA Many EAs can communicate with the OA

8. 7 Infrastructure - Overview

9. 8 Reaction RuleML Reaction RuleML is a branch of the RuleML family that supports actions and events Reaction RuleML is a branch of the RuleML family that supports actions and events When an external agent submits a query to the Symposium planner a Reaction RuleML message must be used When an external agent submits a query to the Symposium planner a Reaction RuleML message must be used In general, when any two agents communicate, Reaction RuleML messages are sent through the ESB In general, when any two agents communicate, Reaction RuleML messages are sent through the ESB Our ESB implementation is MULE Our ESB implementation is MULE

10. 9 Communication Middleware Mule Enterprise Service Bus (ESB) Mule Enterprise Service Bus (ESB) Mule* is used to create communication end points at each personal and organizational agent of Rule Responder Mule* is used to create communication end points at each personal and organizational agent of Rule Responder Mule supports various transport protocols (e.g. HTTP, JMS, SOAP) Mule supports various transport protocols (e.g. HTTP, JMS, SOAP) Rule Responder currently uses HTTP and JMS as transport protocols Rule Responder currently uses HTTP and JMS as transport protocols http://mulesource.com http://mulesource.com * Mule – The open source SOA infrastructure: http://mulesource.com http://mulesource.com

11. 10 Current Rule Engines Prova: Prolog + Java Prova: Prolog + Java OO jDREW: Object Oriented java Deductive Reasoning Engine for the Web OO jDREW: Object Oriented java Deductive Reasoning Engine for the Web

12. 11 Prova Prova is mainly used to realize the organizational agents of Rule Responder Prova is mainly used to realize the organizational agents of Rule Responder It implements Reaction RuleML for agent interaction (event-condition-action rules) It implements Reaction RuleML for agent interaction (event-condition-action rules)

13. 12 OO jDREW OO jDREW is used to realize the personal agents of Rule Responder OO jDREW is used to realize the personal agents of Rule Responder Deployed as Java Servlets Deployed as Java Servlets It implements Hornlog RuleML for agent reasoning (Horn logic rules) It implements Hornlog RuleML for agent reasoning (Horn logic rules)

14. 13 Online Use Case Demo Rule Responder: http://responder.ruleml.org Rule Responder: http://responder.ruleml.org http://responder.ruleml.org RuleML-2007/RuleML-2008 Symposia: http://ibis.in.tum.de/projects/paw/ruleml-2007 http://www.ruleml.org/RuleML-2008/RuleResponder/ RuleML-2007/RuleML-2008 Symposia: http://ibis.in.tum.de/projects/paw/ruleml-2007 http://www.ruleml.org/RuleML-2008/RuleResponder/ http://ibis.in.tum.de/projects/paw/ruleml-2007 http://www.ruleml.org/RuleML-2008/RuleResponder/ http://ibis.in.tum.de/projects/paw/ruleml-2007 http://www.ruleml.org/RuleML-2008/RuleResponder/ Personal agents: Supporting all Chairs Personal agents: Supporting all Chairs Organizational agent: Supporting Symposium as a whole Organizational agent: Supporting Symposium as a whole Onlin e

15. 14 Conclusion Rule Responder was implemented & tested for various use cases ( http://responder.ruleml.org ) and deployed for RuleML-2008 Q&A Rule Responder was implemented & tested for various use cases ( http://responder.ruleml.org ) and deployed for RuleML-2008 Q&A http://responder.ruleml.orgQ&A http://responder.ruleml.orgQ&A Its organizational agents delegate external queries to topic-assigned personal agents Its organizational agents delegate external queries to topic-assigned personal agents It couples rule engines OO jDREW & Prova via Mule middleware and RuleML 0.91 XML interchange format It couples rule engines OO jDREW & Prova via Mule middleware and RuleML 0.91 XML interchange formatOO jDREWProvaRuleML 0.91OO jDREWProvaRuleML 0.91