Semantic agent systems

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  • Consider the following example (a web-enabled method for saving the doomed crew of The Perfect Storm, Junger, 1997). In this story, now a major motion picture, a crew of fishermen is out at sea when weather conditions conspire to create a storm of epic proportions. For various reasons, the crew is unable to get a detailed weather map, and thus miss the fact that the storm is developing right in their way. Instead of avoiding it, they end up at its center with tragic results. How could web agents have helped?As the Captain of the ship goes to call land, a wave hits and his cell-phone is swept overboard. Luckily, he is a savvy web user, and has brought his wireless web device with him as welChecking the weather forecast from a standard weather site, he determines that a storm is coming, but he does not find enough detail for his planning needs. He goes to an agent-enabled geographical server site and invokes the query "Get me a satellite photo of this region of the Atlantic (and draws a box on an appropriate map)." The system comes back a little later with the message displayed in Figure 2. Options range from a picture available on the web (possibly out of date) to other services (that may need special resources) and even future options currently being announced. The captain now chooses an option depending on what resources he has available and what criterion he is willing to accept. Recognizing the gravity of his situation, he invokes the Coast Guard option, and an overflight is scheduled for his GPS location. Seeing the emerging weather, the Coast Guard arranges an emergency pickup at sea, and the sailors are able to go on to fish again some other day.
  • Semantic agent systems

    1. 1. SEMANTIC AGENT SYSTEMSTowards a Reference Architecture for Semantic Agent Systems Applied to Symposium Planning<br />Usman Ali<br />Fredericton, NB<br />1<br />
    2. 2. Outline<br /><ul><li>Background
    3. 3. Organization
    4. 4. Virtual Organization
    5. 5. Organizational Designs
    6. 6. Agent Scenarios
    7. 7. Multi Agent System Frameworks
    8. 8. Conclusion</li></ul>2<br />
    9. 9. Semantic Web Vision<br />3<br />
    10. 10. Agent Scenario<br />Consider a Web-enabled method for saving the doomed crew of The Perfect Storm.<br />How could Web agents have helped?<br />4<br />
    11. 11. Organization<br />"An organization provides a framework for activity and interaction through the definition of roles, behavioural expectations and authority<br />relationships (e.g. control)."<br />5<br />
    12. 12. Virtual Organization<br />"Virtual Organizations are a set of individuals and institutions that need to co-ordinate resources and services across institutional boundaries".<br />6<br />
    13. 13. Linked Data<br />7<br />
    14. 14. Software Personal Assistants<br />Software personal assistants (SPA) are an active research area that one day might change the face of our human organizations.<br />Overload<br />Speed<br />8<br />
    15. 15. Agent basedComputing<br />Agent based computing merges two technologies, namely Artificial Intelligence (AI) and object-oriented distributed computing.<br />9<br />
    16. 16. Importance of Agent oriented thinking<br />As real-world applications are becoming significantly more complex than before. Agent-oriented techniques provide a natural way for modelling complex systems, by decomposing its problem space into autonomous agents and their interactions.<br />10<br />
    17. 17. Agent Centered Versus Organisation CenteredApproach<br />Classical <br />New Approach<br />11<br />
    18. 18. Organization Design<br />Tools?<br />12<br />
    19. 19. Agent Oriented Modelling and Design<br /><ul><li> Scenario</li></ul> A situation in a application involving actors and activities.<br /><ul><li> Structured Thinking</li></ul> Agents start with an overall plan to solve the problem.<br /><ul><li> Unstructured Thinking</li></ul> Actors can start from anywhere and build up a solution.<br />Actors can play roles based on their perception (mental states).<br />13<br />
    20. 20. Multi Agent Frameworks<br />Presentation<br />Searchable<br />14<br />
    21. 21. EMERALD<br />15<br />
    22. 22. RULE RESPONDER<br />16<br />
    23. 23. Organizational Agent<br /><ul><li> The organizational agent represents the goals and strategies shared by each committee chair.
    24. 24. It contains rule sets that describe the policies and regulations of the RuleML Symposium.
    25. 25. Delegates incoming queries to the chair’s PAs.</li></ul>17<br />
    26. 26. Personal Agent<br /><ul><li>A personal agent assists a single chairof the symposium, (semi-autonomously) acting on his/her behalf.
    27. 27. Each personal agent contains a rule-base FOAF-like profile.
    28. 28. It contains a FOAF*-like fact profile plusFOAF-extending rules to encode selected knowledge of its human owner.</li></ul>18<br />
    29. 29. External Agent<br /><ul><li> External agents exchange messages with the OA. They submit queries and receive answers.
    30. 30. End users, as external agents, interact with the OA using a Web (HTTP) interface to the Symposium Planner.
    31. 31. Support for simultaneous external agents</li></ul> Many EAs can communicate with the OA.<br />19<br />
    32. 32. Query Delegation<br />Sponsoring<br />Agents<br />Metatopics<br />Press Release<br />.<br />General Chair<br />.<br />.<br />General Chair<br />Challenge<br />Program Chair<br />.<br />Demos<br />.<br />Program Chair<br />.<br />Challenge Chair<br />Media Partners<br />Challenge Chair<br />.<br />.<br />.<br />.<br />.<br />Publicity Chair<br />Sponsors<br />.<br />.<br />.<br />.<br />.<br />.<br />.<br />.<br />.<br />.<br />.<br />.<br />.<br />Registration<br />Publicity Chair<br />Liaison Chair<br />Visa Letter<br />.<br />.<br />Liaison Chair<br />.<br />Responsible<br />Submissions<br />Accountable<br />Properties:<br />Presentations<br />.<br />.<br />.<br />20<br />
    33. 33. Rule Engines<br />Prova: Prolog + Java<br />OO jDREW: Object Oriented java Deductive Reasoning Engine for the Web<br />21<br />
    34. 34. COMMUNICATION MIDDLEWARE<br />22<br />
    35. 35. MULE ENTERPRISE SERVICE BUS<br />23<br />
    36. 36. Reaction RuleML<br /><ul><li> Reaction RuleML is a branch of the RuleML family that supports actions and events.
    37. 37. When two agents want to communicate, each others’ Reaction RuleML messages are sent through the ESB.
    38. 38. The ESB carries RuleML queries (requests), answers (results), and rule bases to/from agents.</li></ul>24<br />
    39. 39. RuleResponder versus Emerald<br />25<br />
    40. 40. Multi Agent System Interoperation<br />26<br />
    41. 41. REFERENCE ARCHITECTURE OF SYMPOSIUMPLANNER<br />27<br />
    42. 42. 28<br />
    43. 43. Online Use Case Demo<br />http://www.defeasible.org/ruleml2011ijcai/?q=node/25<br />http://de.dbpedia.org/redirects/ruleml/ACE2ReactionRuleML/index.jsp<br />29<br />
    44. 44. 30<br />
    45. 45. 31<br />
    46. 46. Conclusion<br />The SymposiumPlanner and many other applications like it, can truly provide the basis for gradual transformation of our workplace into an efficient and productive environment. <br />32<br />

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