Title: Intelligent agents on the Web
1Intelligent agentson the Web
2 3Several 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
4Cooperative 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
5RETSINA 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
6RETSINA MAS architecture
7The 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
9WebMate 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
11Information 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 16Agents 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
17ADELE
- 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.
21Architecture 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).
25STEVE
- 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
33Learning 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
34Affect recognition
- Posture
- Eye-gaze
- Facial expression
- Hand movement
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36Agents 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 45Electronic commerce
- Transactions - business-to-busines (B2B)
- - business-to-consumer (B2C)
- - consumer-to-consumer (C2C)
- Difficulties of eCommerce
- Trust
- Privacy and security
- Billing
- Reliability
46Consumer'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
47Agents 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)
54IntelliShoper (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
55Anonymizing 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
5612. 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