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 Options range from a picture available on the web (possibly out of date) to other services (that may need special resources) and even future options currently being announced.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 Semantic agent systems Presentation Transcript

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