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Intelligent agents on the Web

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Logic Firefly Finder Jango Kasbah T_at_T IntelliShoper. Need ... Jango (b) Auction bots. Agents that can organize and/or participate in online auctions for goods ... – PowerPoint PPT presentation

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Title: Intelligent agents on the Web


1
Intelligent agentson the Web
  • Adina Magda Florea

2
  • 1. Information agents

3
Several types of information agents
  • Personal agents
  • provide "intelligent" and user-friendly
    interfaces
  • observe the user and learn users profile
  • sort, classify and administrate e-mails,
  • organize and schedule user's tasks
  • in general, agents that automate the routine
    tasks of the users
  • Web agents
  • Tour guides Search engines
  • Indexing agents - human indexing
  • FAQ finders - spider indexing
  • Expertise finders

4
Cooperative information retrieval systems
  • Use information retrieval theory and AI
  • Make information resources available by wrapping
    them with agents capabilities
  • Every agent is expert with its own repository
  • Agents communicate using an ACL

5
RETSINA Reusable Environment for Task-Structured
Intelligent Networked Agents
  • RETSINA is a domain-independent and reusable
    infrastructure on which MAS systems, services,
    and components live, communicate, and interact.
  • RETSINA is an architecture for developing
    distributed intelligent software agents that
    cooperate asynchronously to perform information
    management information gathering, information
    filtering, information integration
  • RETSINA is project developed at the Robotics
    Institute, CMU

6
RETSINA MAS architecture
7
The agent architecture
8
  • Operating environment
  • Platform independent
  • any platform that runs Windows, Linux, or Sun OS
  • PalmPilots
  • Multiple language support Java, C/C, Python,
    LISP, and Perl
  • Network support TCP/IP, wireless, infrared, and
    serial connections

9
WebMate an information search agent in RETSINA
  • WebMate is a personal agent for WWW browsing that
    enhances searches and learns user interests.
  • Information searching
  • trigger pair model
  • document similarity based on relevance feedback

10
  • Trigger Pair Model
  • If a word S is significantly correlated with
    another word T, then (S, T) is considered a
    trigger pair with S being the trigger and T being
    the triggered word
  • Relevance feedback
  • The user identifies relevant pages from an
    initial list of retrieved documents
  • the system analyzes the page using the context of
    keyword (i.e. the words near by)
  • the system finds out the relevant keywords
  • enlarge the user query using the relevant
    keywords

11
Information agents for e-communities (BTexact
Technologies)
  • Personal Agent Framework (PAF)
  • central profile management agent
  • suite of application agents that use profiles in
    conjunction with several information sources
  • Web-based agents

12
  • Profiler Agent
  • one for each user
  • stores interest information in a hierarchy in
    which interests lower in the hierarchy inherit
    their parent interest characteristics
  • transparent for the user
  • each interest
  • private
  • restricted
  • public

13
  • Application agents
  • Bugle uses profile information to generate a
    daily newsletter that contains articles relevant
    to the users interests
  • Grapewine works in the background, periodically
    notifying members via email about other members
    who have similar interest profiles. iVine lets
    the user interactively locate members with
    similar interests. Shows the shared areas of
    interest so the use can decide.
  • Pandora helps broaden users interests via
    collective filtering, suggests new interests for
    members to explore.
  • Radar just-in-time information agent monitors
    the user current activity while, for example,
    authoring a document, and offers relevant
    information resources, news reports, FAQs allows
    interaction with iVine.

14
  • Architecture
  • PAF is integrated with Open Agent Middleware, a
    Java virtual machine platform developed at
    Fujitsu Laboratories
  • Evaluation
  • BTexact internal trials
  • External trial at Concert global venture
    between BT and ATT
  • 1 community around 1000 members

15
  • 2. Agents for e-learning

16
Agents roles in e-learning
  • Enhance e-learning content and experience
  • give help, advice, feedback
  • act as a peer learning
  • participate in assessments
  • participate in simulation
  • personalize the learning experience
  • Enhance LMSs
  • facilitate participation
  • facilitate interaction
  • facilitate instructors activities

17
ADELE
  • Pedagogical agents developed by Center for
    Advanced Research in Technology for Education
    (CARTE) at USC / ISI to assist students in
    working through course materials
  • The lead character, an agent named Adele (Agent
    for Distance Learning Environments), is a
    pedagogical agent designed to work with Web-based
    educational simulations.

18
  • Adele consists of a pedagogical agent and a 2D
    animated persona, which is implemented as a
    web-based Java applet.
  • Adele
  • adapts the presentation of the material as needed
  • monitors students progress
  • provides feedback, hints and rationales to guide
    student actions
  • references relevant material
  • evaluates student performance by probing
    questions.
  • She is used in two medical education systems
    case-based diagnosis and trauma care.

19
  • Simulations created for the course in diagnostic
    skill development presents the student with
    actual cases, including patient history, results
    of exams, lab tests, x-rays, CT scans and other
    diagnostic imaging methods.
  • By questioning and examining the virtual
    "patient" and studying clinical data, the student
    is able to practice diagnostic skills.

20
  • Trauma care is a collaborative activity -
    physicians and paramedics work with other
    emergency response personnel.
  • Adele functioning includes the notion of
    "situations".
  • A situation is a high-level description of an
    "interesting state" along with a description of
    steps to take in that situation.
  • The animated persona is a Java applet. It can be
    used alone or with a Web page-based JavaScript
    interface, or incorporated in larger simulations.

21
  • ADELEs architecture

Architecture of single user system. In the
multi-user system, RE is server-based, as is the
Session Manager Student model, case task plan,
initial state Student record of actions
22
  • Task representation
  • Task plan task steps and their dependencies,
    step rationale
  • task steps object-oriented data structures
    processed by Adeles Java-based reasoning engine
  • Reasoning engine runs in 3 modes
  • restricts unsolicited input Hint, Why
  • practice mode Hint
  • exam Adele is not available
  • Situation triggers a plan
  • Situation plans are pre-authored
  • Adeles reasoning ? situation-monitoring task
  • ? situation-based reasoning.

23
  • Pedagogy
  • Situation-based reasoning allows the recognition
    of pedagogical opportunities
  • ask questions related to a particular task
  • give feedback to chosen answers
  • ask follow-up questions
  • give references significant to a particular task
  • verify correctness of plan step order
  • records the students actions
  • analyze students record and provides domain
    appropriate feedback (e.g., evaluation of
    diagnosis, evaluation of diagnostics costs,
    evaluation of the steps taken).

24
  • Adeles persona
  • Uses gaze and gestures to react to students
    actions repertoire of facial expressions and
    body postures that represent emotions surprise,
    disappointment, etc.
  • Senses users mouse pointing, turns her head and
    looks toward that point.
  • She has also a pointer that she can use to point
    to objects in other windows.
  • Animations are produced from 2-dimensional
    drawings gt makes possible to run on a variety of
    desktops (no 3D graphics needed).

25
STEVE
  • Developed at Information Science Institute, USC
  • Learning environment simulation of the naval
    training facility in Great Lakes, Illinois
  • Steve a 3D pedagogical agent
  • Training a 3D, interactive, simulation
    environment

26
  • Students and Steve agents are immersed in the
    simulation environment
  • Students 3D immersive view of the virtual world
    through a head-mounted display (HMD) and
    interacts with the world via data gloves
  • Lockheed Martins Vista Viewer software uses data
    from a position and orientation sensor on the HMD
    to update the students view as he moves around
  • Additional sensors on the glove keep track of the
    students hands and Vista sends messages when the
    student touches virtual objects

27
  • Humans and agents communicate through spoken
    dialogue
  • An agent speaks to a person by sending a message
    to the persons text-to-speech software
    broadcasts the utterance through the headphones
    mounted on the HMD
  • Entropics TrueTalk for speech synthesis
  • Students speak to the microphone on the HMD -
    sends the utterance to the speech recognition
    software semantic representation of the
    utterance to the agents.
  • Entropics GrapHvite for speech recognition

28
  • Steves cognitive architecture

Task knowledge
Pedagogical capabilities
Perception snapshots important events
Abstract motor commands
Soar rules
Spatial properties
Motor Control
Perception
Relevant events
Detailed motor commands
Message Dispatcher
Interface components
Simulator
Visual, audio effects
29
  • Separation between domain independent
    capabilities and domain specific knowledge
  • Perception, cognition and motor control modules
    general capabilities independent of a particular
    domain
  • planning
  • replanning
  • plan execution
  • assessment of students actions
  • question answering (What should I do next?, Why?)
  • episodic memory
  • communication
  • control of human figure

30
  • Course author specifies the domain knowledge in a
    declarative language
  • Domain knowledge
  • perceptual knowledge
  • knowledge about objects in the virtual world,
    objects simulation attributes and spatial
    properties
  • task knowledge
  • procedures for accomplishing domain tasks and
    text fragments for talking
  • Tasks set of steps
  • ordering constraints
  • causal links
  • hierarchical planning

31
  • Steve acts as a tutor or learning companion
  • Steve was extended to support team training
  • Steve agents can play two roles
  • tutor for an individual team member
  • can substitute for missing team members

32
  • Tasks were extended with roles for different
    participants
  • Planning is extended by mapping task steps to
    team roles roles are assigned during plan
    creation
  • Team task request
  • each Steve agent involved in the task as a team
    member or instructor uses his task knowledge to
    construct a complete task model
  • New types of actions - a speech act from one team
    member to another
  • each speech act appears as a primitive action in
    task description

33
Learning Companion that recognizes affect
  • MIT Media Lab
  • Affective states significant to learning
    anxiety, worry/boredom, indifference, interest,
    curiosity, confident, etc.
  • on-goal and off-goal

34
Affect recognition
  • Posture
  • Eye-gaze
  • Facial expression
  • Hand movement

35
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36
Agents for LMSs
  • Knowbots (or Knowledge Robots) created to
    automate the repetitive tasks of human
    facilitators in online workshops
  • System developed at ALN Center at Vanderbilt
    University, Nashville, TN

37
  • System architecture
  • 5 components knowbots, the learner, the
    knowledge base, the repository of assignments and
    the interface with the facilitator.
  • Knowbots sit between the instructor and the
    learner, mediating the interaction.

38
  • 3 types of knowbots
  • scheduled - sends a reminder and a report to each
    participant upon completion of a scheduled check
  • on-demand - invoked by the learner these
    knowbots return results immediately to the
    requesting user
  • submission helper - for submission of an
    assignment that assists the user in submitting
    the assignment they also notify the facilitator
    when the submission is made.

39
  • Knowbot structure
  • user-interface agents
  • checker agents (agents that check submissions)
  • e-mail agents
  • knowledge base modules.
  • User-interface agents - graphical interface,
    web-based agents assure user interaction with
    the knowbot
  • Execute the checker agents by request
  • Present information to the user
  • Provide appropriate interface to execute actions
    such as requests for help
  • Communicate with other agents and with the
    knowledge base.

40
  • Email agents are responsible for generating,
    composing, organizing, and sending e-mails to
    both the instructor and the participants.
  • Examples of e-mails that are generated and sent
    to the participants are
  • the assignment-status report
  • the assignment reminder and notification
  • the message responding to a request for help.
  • The e-mail agents compose the content of the
    e-mail by retrieving data from the knowledge base.

41
  • Checker agents are responsible for checking
    assignments for the participants.
  • The agents can be invoked either by the scheduler
    or by the participant through the user-interface
    agents.
  • determine the completion status of the assignment
    based on the pre-defined knowledge of
    requirements for assignment completion.
  • record the results and access the knowledge base
    through the established Open Database
    Connectivity (ODBC) using the Cold Fusion Markup
    Language (CFML).
  • determine what particular knowledge each
    participant needs in order to complete the
    assignment.

42
  • Knowbots in the system
  • Posting knowbot - looks for two types of messages
    posted in the specified forum by participants
    one is a self-introduction message, the other is
    a reply-to-another message. The knowbot then
    sends a reminder and the results of the scheduled
    check via e-mail to the participants.
  • S,OD
  • Course Review knowbot - looks for at least 3
    course-reviewed messages posted in 3 different
    threads by the participants and sends a reminder
    and the result of the checking by e-mail to the
    participants.
  • S,OD
  • Basic HTML knowbot - checks the status of each
    participant's personal homepage to determine if
    it contains the required elements such as mail-to
    tag, bulleted list, etc.
  • S,OD

43
  • Topic knowbot - is invoked by the student and
    determines if at least one message has been
    posted into the specified forum in the
    conferencing system about the required topic. The
    result is displayed to the student. OD
    only
  • Multimedia knowbot - Each participant submits
    information via a knowbot. The knowbot notifies
    the workshop facilitator about the submission,
    provides a template for the facilitator to check
    the participant's work, stores the results into
    the database and sends a notification e-mail to
    report the result to the participant.
  • Submission Helper
  • Discussion Builder knowbot - Same functionality
    as Multimedia knowbot
  • Submission Helper

44
  • 3. Agents for e-commerce

45
Electronic commerce
  • Transactions - business-to-busines (B2B)
  • - business-to-consumer (B2C)
  • - consumer-to-consumer (C2C)
  • Difficulties of eCommerce
  • Trust
  • Privacy and security
  • Billing
  • Reliability

46
Consumer's buying behavior
  • Consumer's Buying Behavior (CBB) research - a
    number of models of the consumer's behavior
  • CBB - Guttman e.a., 1998
  • Need identification
  • Product brokering
  • Merchant brokering
  • Negotiation
  • Purchase and delivery
  • Product service and evaluation
  • - some stages may overlap

47
Agents as mediators in eCommerce
  • Persona Bargain
  • Logic Firefly Finder
    Jango Kasbah T_at_T IntelliShoper
  • Need
  • identification
  • Product
  • brokering
  • Merchant
  • brokering
  • Negotiation
  • Purchase
  • and delivery
  • Product
  • service

48
(a) Comparison shopping agents
  • Search online shops to find products, merchants
    and best deals
  • Product brokering
  • Techniques
  • feature-based filtering feature keywords
  • collaborative filtering similarities between
    users profiles
  • constraint-based filtering specifying
    constraints (price, date limit)

49
  • Product brokering
  • let the users create preference profiles
  • allows shoppers to specify constraints on a
    product and scores the products
  • CSP engine hard constraints and soft constraints
  • 1988 ? AOL
  • helps consumers find products (alert) (Ringo
    books, CDs)
  • ACF Automated Collaborative Filtering
  • identifies the shopper's "nearest neighbours" and
    offers products highly rated by them
  • 1998 ? Microsoft

50
  • Merchant brokering
  • finds specifications and product reviews
  • makes recommendations to the user
  • submit queries to vendors sites and interpret
    results to identify lowest price items
  • monitors "what's new" lists, watches for special
    offers
  • automates the building of wrappers to parse
    HTML docs and extract products features
  • Web pages are different exploits
  • ? Navigation regularities (easy to find
    products)
  • ? Corporate regularities (similar looknfeel)
  • ? Vertical separation (use of white spaces)
  • 1999 ? Excite

51
  • (b) Auction bots
  • Agents that can organize and/or participate in
    online auctions for goods
  • Aim develop a Web-based system in which users
    can create their own agents to buy and sell goods
    on their behalf
  • User options
  • Create a new buying agent
  • Create a new selling agent
  • See currently active agents
  • Create a new finding agent
  • Browse the marketplace for active agents

52
  • Selling agent parameters set by the user
  • - desired date to sell the good
  • - desired price to sell the good
  • - minimum price to sell at
  • - "decay" function of the price over time to
    determine the current offer price
  • anxious - linear function
  • cool headed - quadratic function
  • frugal - exponential function
  • Buying agent parameters set by the user
  • - date to buy the item by
  • - desired price
  • - maximum price
  • - "growth" function of price over time

53
  • Integrates product brokering, merchant brokering,
    and negotiation
  • User agents negotiate across multiple attributes
    of a transaction, e.g., warranty length and
    options, shipping time and cost, service
    contract, return policy, quantity, accessories,
    credit options, payment options
  • Agents quantify those aspects using a
    multi-attribute utility function
  • Today Frictionless Commerce applies the
    technology to B2B markets (e-sourcing)

54
IntelliShoper (U. Iowa)
  • Integrates product brokering, merchant brokering,
    and negotiation
  • Goals
  • Customize behavior adaptively by learning users
    preferences
  • Provides assistance by remaining autonomous from
    both customer and vendors
  • Protect shoppers privacy by concealing their
    identities and behavior from vendors

55
Anonymizing server
IntelliShoper server
Privacy Agent
Monitor Agent
Learning Agent
Vendor plug-ins
Sequence of shopping assistance activities
  • The user creates an account and one or more
    personae
  • The user takes on a persona
  • The persona initiates a shopping session by
    submitting a query to the LA
  • The LA stores the users request in the database
  • The LA uses vendors plug-ins to send requests to
    vendors
  • Results from vendors are parsed through the
    vendors plug-ins
  • IS stores the result in the database
  • The LA uses the persona profile to rank the hits
  • The LA presents the results to the persona
  • The PA forwards the results to the user
  • The user can further interact with the LA

Basic interaction loop
56
12. The MA loads standing queries from the
database 13. The MA uses vendor plug-ins to check
for any new results from the vendors 14. IS
parses new and updated hits 15. IS stores the
hits in the database until the users logs in again
Occurs offline
  • Privacy Agent
  • lets the user take a shopping persona
  • hides identity user info (permutation, stripping
    of IP addresses, encryption, decription)
  • Shopping Persona
  • becomes the public user
  • 2 aims protect user privacy multiple profiles
  • Interface
  • create new persona preferred sites
  • see current personae (name, what to buy,
    preferences)
  • submit a new shopping request via the query
    interface
  • view hits

57
  • Interface with the vendors Web sites
  • submitting queries
  • parsing results
  • Language for specifying vendor dependent logic
    based on XML and inspired by Apples Sherlock
    engine
  • Personas Profile
  • Preference
  • Keywords
  • Relevant features numeric (discretized) and
    textual (keywords)
  • Updating the user profile
  • Temperatures for features
  • Updates temperatures after any user action
    related to a given hit

58
  • Temperature update
  • T(t1) (1 - ?) T(t) ? ?T
  • 5 possible actions
  • Buy string positive feedback ?T 2
  • Browse weak positive feedback ?T 1
  • Skip weak negative feedback ?T -1
  • Remove strong negative feedback ?T -2
  • Status
  • - Research project
  • - Current prototypes eBay, Yahoo and Amazon
    auctions
  • - Research on the development of intelligent
    wrappers that could automate submitting queries
    and parsing results.

59
  • References
  • M. Wooldrige. An Introduction to MultiAgent
    Systems, John WileySons, 2002, Ch.11, p.243-266.
  • R. Guttman, A. Mokas, P. Maes. Agents as
    mediators in electronic commerce. In Intelligent
    Information Agents, M. Klush (Ed.), Springer
    Verlag 1999, p.131-152.
  • P. Noriega, C. Sierra. Auctions and multi-agent
    systems. In Intelligent Information Agents, M.
    Klush (Ed.), Springer Verlag 1999, p.153-175.
  • W. Brenner, R. Zarnekov, H. Witting. Intelligent
    Software Agents, Springer Verlag, 1998, Ch.6,
    p.267-299.
  • K. Sycara, Massimo Paolucci, Joseph Giampapa
    The RETSINA MAS Infrastructure TechReport
    CMU-RI-TR-01-05 2001
  • K. Chen, K. Sycaca WebMate A Personal Agent
    for Browsing and Searching The Robotics
    Institute, Carnegie Mellon University 1998
  • K. L. Clarc, V.S. Lazarou A Multiagent System
    for Distributed Information Retrieval on the
    World Wide Web 1997
  • F. Menczer, W. Street, A. Monge. Adaptive
    assistants for customized e-shopping. IEEE
    Intelligent Systems, Nov/Dec 2002, p.12-19.

60
  • References - continued
  • S. Case, N. Azarmi, M. Thint, T. Ohtami.
    Enhancing e-communities with agent-based systems.
    IEEE Computer, July 2002, p.64-69.
  • R. Ganeshan, W.L. Johnson, E. Shaw, and B.P.
    Wood. Tutoring Diagnostic Problem Solving , In
    Proceedings of the Fifth Int'l Conf. on
    Intelligent Tutoring Systems, 2000.
  • E. Shaw, W.L. Johnson, and R. Ganeshan.
    Pedagogical Agents on the Web. In Proceedings of
    the Third Int'l Conf. on Autonomous Agents, pp.
    283-290, May, 1999.
  • C. Thaiupathump, J. Bourne, J.O. Campbell.
    Intelligent Agents for Online Learning. JALN
    Volume 3, Issue 2 - November 1999.
  • ADELE http//www.isi.edu/isd/ADE/ade-body.html
  • Ganeshan, R., Johnson, W.L., Shaw, E., and Wood,
    B.P. Tutoring Diagnostic Problem Solving , In
    Proceedings of the Fifth Int'l Conf. on
    Intelligent Tutoring Systems, 2000
  • Shaw, E., Ganeshan, R., Johnson, W.L., and
    Millar, D. Building a Case for Agent-Assisted
    Learning as a Catalyst for Curriculum Reform in
    Medical Education, In Proceedings of the Int'l
    Conf. on Artificial Intelligence in Education,
    July, 1999
  • Shaw, E., Johnson, W.L., and Ganeshan, R.,
    Pedagogical Agents on the Web. In Proceedings of
    the Third Int'l Conf. on Autonomous Agents, pp.
    283-290, May, 1999

61
  • References - continued
  • STEVE http//www.isi.edu/isd/VET/vet-body.html
  • Rickel, J., Johnson, W.L., Virtual Humans for
    Team Training in Virtual Reality, in Proceedings
    of the Ninth International Conference on AI in
    Education, pp. 578-585, July 1999, IOS Press.
    (Received Best Paper award.)
  • Rickel, J., Johnson, W.L., Intelligent Tutoring
    in Virtual Reality A Preliminary Report, in
    Proceedings of the Eighth World Conference on AI
    in Education, pp. 294-301, August 1997, IOS
    Press.
  • Rickel, J., Johnson, W.L., Integrating
    Pedagogical Capabilities in a Virtual Environment
    Agent, in Proceedings of the First International
    Conference on Autonomous Agents, pp. 30-38,
    February 1997.
  • Survey of Work on Animated Pedagogical Agents
  • W.L. Johnson, J.W. Rickel, and J.C. Lester.
    Animated Pedagogical Agents Face-to-Face
    Interaction in Interactive Learning Environments.
    International Journal of Artificial Intelligence
    in Education 1147-78, 2000.
  • Johnson, W.L., Pedagogical Agents, invited paper
    at the International Conference on Computers in
    Education. Also to appear in the Italian AI
    Society Magazine.

62
  • Online references
  • Product brokering
  • Net Perceptions http//www.netperceptions.com
  • CDNOW http//www.cdnow.com
  • Merchant Brokering
  • Priceline http//www.priceline.com
  • Internet Auction List more than 2500 auction
    companies listed (as of 2002)
  • http//www.internetauctionlist.com
  • Auctions bots
  • IntelliShoper http//myspiders.biz.uiowa.edu/fil
    /intellishoper
  • Software Agents Group, MIT Media Lab
  • http//agents.media.mit.edu/projects/
  • Kasbah - Chaves, Maes, 1996
  • Tête-à-Tête - Guttman, Maes, 1998
  • Firefly - Shardanand, Maes, 1995
  • Auction Agents for the Electric Power Industry
  • http//www.agentbuilder.com/Documentation/EPRI/i
    ndex.html
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