IA by dandanhuanghuang


									    Intelligent Agent
             David Yang
National Kaohsiung Normal University
            Purpose of Presentation
• To overview the rapidly evolving area of
  software agents

• To present the real challenges and
  potential benefits of agents research

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• Background Introduction
• Historical Review
• Agent-Oriented Software
• Semantic Web & Agent
• Conclusion

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             Video Show
• Knowledge Navigation (Apple, 1988)
• Computational AI is too limited and a
  controversial issue
• Weak AI (Searle, 1980): Computer robots
  can simulate human capabilities but will
  not replicate mentality
• Strong AI (Sloman,1992): AI systems will
  have mental states and processes

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              Agents (Norvig, 1995)
                      percepts                   sensors

      environment                                          ?


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      A Unifying Theory of Arch.

•   perception,
•   – central states and processes,
•   – action mechanisms
•   (All with fuzzy boundaries)

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                Another Common Arch.

•    Evolutionary age (reactive oldest from brain research
•    – Level of abstraction of processing
•    – The types of control functions, and mechanisms used
•    – The forms of representation used

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 CogAff Schema(Sloman,2001)

• CogAff :defines only possible components and links –
  not all need be in all organisms, or all robots
• An ‘ecosystem’ of mind: A grid of co-evolving sub-
  organisms each contributing to the niches of the others.

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       Why do we need the intelligent
            agent technology
• Information explosion and overload due to
  the popularity of internet technology
      – information supply (provider)
      – Information demand (consumers)
• Personal assistance of information access
  and utilization

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       Why do we need the intelligent
         agent technology(Cont.)
• Enhance efficiency of communication and
  interaction among people
• Construction of virtual agent-based
  communities and societies

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            What is Software Agent?
• Particular type of agent
• Inhabiting computers and network
• Assisting users with computer-based tasks

                       Interacts with

        Applications                                    collaborate

                        Interacts with               Agent

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             Software Agents: History
• Agents research dates from the early days
  of AI work

• Carl Hewitt’s actor model (1997)
            “a self-contained, interactive and
            concurrently-executing object, with some
            encapsulated internal state and which could
            respond to messages from other similar
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            Software Agents: History

              Context of agents research

                       Distributed AI

    Distributed          Agents and          Parallel AI
  Problem Solving   Multi-agent Systems

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 Motivation for Agents Research
• Benefits from DAI

       Modularity which reduces complexity

       Speed due to parallelism

       Reliability due to redundancy

       Flexibility new tasks are composed more
        easily from the more modular organisation

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 Motivation for Agents Research
• Benefits from AI

       Operation at the knowledge level

       Easier maintenance

       Reusability

       Platform independence

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               Software Agents:
            Strand One: 1977- date
• Concentrated on deliberative-type
    agents with symbolic internal models

• Addressed macro issues emphasizing the
  society of agents over individual agents

• Had the goal to specify, analyze, design
  and integrate systems comprising of
  multiple collaborating agents
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            Strand 1 Agents Work:
                macro issues
• The interaction and communication
  between agents (Speech Act Theory)

• The decomposition and distribution of
  tasks among agents

• Coordination and cooperation

• Conflict resolution via negotiation

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             Software Agents:
            Strand Two (1990 )

• research and development of agent
  theories, architectures and languages

• a significant broadening of the typology of
  agents being investigated

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            Agent Applications
• workflow management          • digital libraries
• network management           • personal digital
• air-traffic control            assistants
• business process             • diary management
  engineering                  • e-mail filtering
• smart databases              • information
• command and control            management
• education                    • data mining
                               • electronic commerce

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            Agent Applications

    “in 10 years time most new IT
    development will be affected, and many
    consumer products will contain embedded
    agent-based systems” (Guilfoyle 1995)

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            Don’t constantly need       Are able to work
            instructions                    unaided

  information with                                       Improve their
     each other.                                          actions with
                 Cooperate                         Learn   experience
  Able to agree on

                     Software Agents
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                Typology of Agents

                                             Learning Agents
                                             Learning Agents
             Cooperate              Learn

Collaborative                                  Agents
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   Classification of intelligent agents

• Classify by mobility
      – Stationary
      – Mobile
• Classify by architecture
      – Deliberative
      – Reactive
      – social

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            Classification of intelligent
• Classify by attributes
      – Collaborative agents
      – Interface agents
      – Learning agents
• Classify by roles
      – Match marker
      – Information gathering
      – Tutorial agents, etc

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            What is an Agent?
• A component of software and/or hardware
  which is capable of acting exactingly in
  order to accomplish tasks on behalf of its

• An umbrella term which covers a range of
  other more specific agent types

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  Characteristics of software agents

• Proactive: goal-oriental
• Autonomy: bounded rationality
• Social: communication, cooperation,
  team/coalition formation
• Adaptive: learning from environment and
  other agents
• Mobility: interoperability, navigation,
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            What’s new in software agent
• A paradigm shift of information utilization
  from direct manipulation to indirect access
  and delegation
• A kind of middleware between information
  demand (client) and information supply
• A software that has autonomous,
  personalized, adaptive, mobile,
  communicative abilities

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 What intelligent agents can do?
•   Internet data gathering and retrieval
•   Electronic news and mail filtering
•   Calendar management and meeting scheduling
•   Work-flow assistants
•   Making travel arrangement
•   Event monitoring
      – Alerting users to investment opportunities
      – Alerting doctors on emergency events of patients

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            Typical examples for human
• Electronic commerce
      – Buyer, sellers, brokers, banks…..
• Virtual enterprise
      – Managers, manufacturers, engineers,
• Digital library
      – Librarians, users

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            Typical examples for human
• Virtual school
      – Teachers, students, classmates, staff
• Virtual hospital
      – Physicians, staff, patients
• Electronic government
      – Policy makers, officers, policemen, citizens

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  Impacts of the software agents on
        information services
• Information services become interactive
  and personalized
• The service provider can record and
  induce from the user’s behaviors
• The service provider can easily customize
  its services to different customers with low

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  Impacts of the software agents on
     information services(Cont.)
• Enhance the quality
• Reduce the cost of information services
• Provider integrated services

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            Environment of agents
•   Accessible versus inaccessible
•   Deterministic versus nondeterministic
•   Episodic versus nonepisodic
•   Static versus dynamic
•   Discrete versus continuous

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    How AI techniques used in the IA
•   Knowledge representation and inference
•   Planning
•   Machine learning/data mining & discovery
•   Common sense reasoning
•   Uncertainty reasoning

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   How AI techniques used in the IA
• Distributed (multiple agents) problem
• Distributed Constraint Satisfaction
  Problem (DCSP)
• Intelligent man/machine interface
• User modeling--personalized

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         Comparison between expert
        systems and intelligent agents
• Expert systems address less
  communication and coordination issues
• Expert systems tend to knowledge-driven
  and address less inferring joint intention
  and team behaviors
• Address nothing about mobility
• Autonomous agents versus consultant
  through dialogue

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       Types of agent Architecture
•   Logic-base agents
•   Reactive agents
•   Belief-Desire-Intention(BDI) agents
•   Layered architectures

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     Process of Simple Reflex Agent
 function SIMPLE-REFLEX-AGENT( percept) returns
  static: rules
  state ←INTERPRET-INPUT(percept)
  rule ←RULE-MATCH(state, rules)
  action ←RULE-ACTION[rule]

 return action

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            BDI Architecture
                                     1. function action( p : P):A
                                     2. begin
                                     3.   B :=br f( B,p)
                                     4. D :=options(D,I);
                                     5. I :=f ilter( B,D,I);I
                                     6. return execute( (I);
                                     7. end function action

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            Layered Architectures

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Software Engineering
            Research Topics on AOSE
• UML and agent systems
• Agent-oriented requirements analysis and
• Software development environments and CASE
  tools for AOSE
• Standard APIs for agent programming such
• Formal methods for agent-oriented systems,
  including specification and verification logics

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            Research Topics on AOSE
• Engineering large-scale agent systems
• Experiences with field-tested agent
• Best practice in agent-oriented
• Market and other economic models in
  agent systems engineering
• Practical coordination and cooperation
  frameworks for agent systems
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  Knowledge Representation for the
     “Semantic Web” with Agent

                                         DAML-L (Logic)

                                 DAML + OIL (Ontology)

                            RDFS (RDF Schema)

             RDF (Resource Description Framework)

            XML (Extensional Markup Language)

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               The Evolving Web
 • Locating Resources
    – free text & keyword search  semantic search
 • Web Users
    – primarily humans  both humans and machines
 • Web Tasks & Services
    – a place to find things  a place to do things

 Semantics is the Core Requirement
       – web content with no semantics  with semantics

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   Agents and the Semantic Web
 • Semantic Web: killer ‘app’ for agents?
 • Agents need to communicate and understand
    – Advertise and require capabilities
    – Locate meaningful information resources on
      web & combine them in meaningful ways to
      perform tasks
    – How to interpret communication acts?
 • But what do we mean by the Semantic Web?

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             What do we mean by
• Semantics of What?                                  Implicit
   – language? term? expression?
   – communication protocol?
   – domain ontology & markup!
• Plicity: Are the semantics implicit or explicit?
• Formality: How are semantics expressed?
• Semantics Processing: Who are they for?
   – human only – fully manual
     – human and computer – partially automated
     – computer only – fully automated
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• Implicit: based on human consensus, shared
   – Typical XML tags
       • <price>          200      </price>
       • <address>         …       </address>
       • <delivery-date> … </delivery-date>
   – Used by screen-scrapers, wrappers
   – Rife with ambiguity.
• Informal: only humans can use (until NLP solved)
   – Text specification document for HTML e.g. <h2>
   – UML semantics document
   – Java language definition, for compiler writers
   – Still ambiguous
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 • ‘Formal Comments’
     – Semantics of FIPA ACL ‘inform’ in modal logic
     – Formal definitions in any requirements spec (e.g. Z)
     – Many axioms in Ontolingua ontologies
     – Much less ambiguous
     – Still error-prone, human in the loop.
 • Automated
     – RDF(S), DAML+OIL term definitions
       e.g. mammal, date
       – How does the machine process the semantics?

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  Machine Processible Semantics
• How can an agent learn the meaning of a term?
• Procedural Semantics
   – How does an agent system know what to do when it sees
     the term ‘inform’
   – The (possibly informal) semantics of ‘inform’ is
     embedded in a procedure by a human.
   – The system places a call to the procedure when it
     encounters ‘inform’.
   – The ‘meaning’ of ‘inform’ is what happens when this
     procedure is called.
• Machine processible semantics? – perhaps.

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  Machine Processible Semantics
• Learning the meaning of a term from a formal
  declarative specification of the semantics…
• General case: no assumptions, nothing shared
   – all symbols might as well be in ‘Greek’ script
   – no knowledge of language syntax, or semantics
   – Cryptography, impossible to automate
   – So, we have to cheat…
• We must make some assumptions…

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             Assumptions: Language
• Shared language syntax and semantics,
   – e.g. KIF, RDF(S), DAML+OIL

• But: may have incompatible assumptions in
   – Time point, vs. time interval
   – Agent can never incorporate meaning of new
     term in its axioms.

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              More Assumptions:
• Logical compatibility as well as language.
• But: Different people build different ontologies
        for the same domain.
   – Two terms, same meaning, or vica versa;
   – Same concept modeled at different level of detail;
   – Different language primitives used for same concept;
       • e.g. red an attribute, or RedThings a class.
• Computationally intractable to determine if two terms actually
  mean the same thing.
   – I.e. have same set of models

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         More Assumptions: Sharing
• Term explicitly mapped to a shared concept
   – Encounter new term, leprechaun, a subclass of mammal.
   – ‘mammal’ defined in shared animal ontology in OIL.
• Machine can learn something about meaning.
   – I.e. there are now more things that it cannot be.
   – Still plenty of scope for ambiguity;
   – Definition of mammal in OIL can never be complete.
• Can do some inference
   – e.g. for search application looking for content about

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             Processing Semantics
• Relies on a formal semantics of OIL to infer semantics
  of terms and expressions in OIL.
• OIL semantics is for humans
   – it helps build inference engines;
   – not machine processible.
• Humans may still embed some meaning in code
   – May be dangerous to do so – or –
   – May be necessary to do so…
• The shared concept referred to may not be formally
  defined (e.g. Dublin Core terms)

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            Definition of Ontology
• An ontology is a formal, explicit
  specification of a shared conceptualization.
      – Formal
      – Explicit specification
      – Shared
      – conceptualization

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      What is a conceptualization?

                         b               d

                         c               e

                    Scene 1: blocks on a table

            Conceptualization of scene 1:
            <{a, b, c, d, e }, {on, above, clear, table }>

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What is a conceptualization?

                        a               d

                        b               e

            Scene 2: a different arrangement of blocks

                The same conceptualization?

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What is a conceptualization?
• Conceptualization: the formal structure of
  reality as perceived and organized by an
  agent, independently of:
      – the vocabulary used (i.e., the language used)
      – the actual occurrence of a specific situation
• Different situations involving the same objects,
  described by different vocabularies, may share
  the same conceptualization.
            LE   apple
                                         same conceptualization
            LI   mela
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  An ontology model for FIPA agent
• Frames
      – Frames are used to represent concepts.
• Relations
      – KIF relations, are used to represent concepts
        we will simply described by a value and to
        define concepts as combination of other

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    What are we/agents going to use
            ontologies for?
• To share common understanding of
  conceptual models
• To enable knowledge re-use
• To make assumptions about
  knowledge/domain models explicit
• An ontology describes the concepts and
  relationships that can exist for an agent or
  community of agents

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              Human’s Talking
• I am a graduate student at GIAE in NKNU.
• Keyword:
      – student
      – graduate
      – GIAE
                                       Point ?
      – NKNU

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    Domain and scope of the ontology
Domain : Student
Scope : Classmate of senior high school
Class student {
   String School;
   String Institute;
   String Grade;

    public String getSchool() { return School; }
    public void setSchool(String s) { School = s; }
    public String getInstitute() { return Institute; }
    public void setInstitute (String d) {Institute = d; }
    public String getGrade() { return Grade; }
    public void setGrade (String g) { Grade = g; }

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        Reusing existing ontologies
•   Ontolingua ontology library
•   DAML ontology library
•   UNSPSC(United Nation)
•   RosettaNet(Communication)
•   DMOZ(Medicine)

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            Ontology Agent

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        Enumerate important terms
• Educational resources
      – IEEE’s Learning Technology Systems
        Architecture (LTSA)
      – Metadata
            • Dublin Core (DC) Metadata
            • Learning Objects Metadata (LOM)
      – SCORM Metadata
            • IEEE LTSC Learning Objects Metadata
            • IMS Learning Resource Metadata XML Binding

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        Important Lesson Plan-related
• Course’s catalog
• Catalog’s entry
• A entry’s title, format, location,
• Entry’s relation
      – Kind
      – resource

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     Define the classes and the class
• approaches:
      – Top-down
      – Bottom-up
      – Combination
• Note : Only define the classes

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   Define the properties of classes--
• Intrinsic
• Extrinsic
• Relationships to other individuals

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            Define facets of the slots
• Slot cardinality
• Slot-value type
      – String
      – Number
      – Boolean
      – Enumerated

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• The development process of Intelligent
  agents is evolutionary
• Agents combine and force with semantic
  web for information retrieval
• Agent Communication Language(ACL)
  based upon KQML (Knowledge Query
  Markup Language)
• Ontology focuses on conceptual
  development beyond logics

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