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Intelligent agents on the Web
Intelligent agents on the Web
IDC (http://www
IDC (http://www
1. Basic notions of agents and MAS
1. Basic notions of agents and MAS
Agents characteristics
Agents characteristics
Motivations for agents
Motivations for agents
Agents vs Objects
Agents vs Objects
Multi-agent systems
Multi-agent systems
Multi-agent systems
Multi-agent systems
Multi-agent systems
Multi-agent systems
Cognitive agents
Cognitive agents
Premises of cognitive agents
Premises of cognitive agents
Reactive agents
Reactive agents
2. Information agents
2. Information agents
Cooperative information retrieval systems
Cooperative information retrieval systems
RETSINA: Reusable Environment for Task-Structured Intelligent
RETSINA: Reusable Environment for Task-Structured Intelligent
RETSINA MAS architecture
RETSINA MAS architecture
The agent architecture
The agent architecture
WebMate – an information search agent in RETSINA
WebMate – an information search agent in RETSINA
Trigger Pair Model If a word S is significantly correlated with
Trigger Pair Model If a word S is significantly correlated with
Information agents for e-communities (BTexact Technologies)
Information agents for e-communities (BTexact Technologies)
Profiler Agent one for each user stores interest information in a
Profiler Agent one for each user stores interest information in a
Application agents Bugle – uses profile information to generate a
Application agents Bugle – uses profile information to generate a
3. Agents for e-learning
3. Agents for e-learning
ADELE
ADELE
Adele consists of a pedagogical agent and a 2D animated persona, which
Adele consists of a pedagogical agent and a 2D animated persona, which
Simulations created for the course in diagnostic skill development
Simulations created for the course in diagnostic skill development
Trauma care is a collaborative activity - physicians and paramedics
Trauma care is a collaborative activity - physicians and paramedics
ADELE’s architecture
ADELE’s architecture
Task representation Task plan = task steps and their dependencies,
Task representation Task plan = task steps and their dependencies,
Pedagogy Situation-based reasoning allows the recognition of
Pedagogy Situation-based reasoning allows the recognition of
Adele’s persona Uses gaze and gestures to react to student’s actions –
Adele’s persona Uses gaze and gestures to react to student’s actions –
STEVE
STEVE
Students and Steve agents are immersed in the simulation environment
Students and Steve agents are immersed in the simulation environment
Humans and agents communicate through spoken dialogue An agent speaks
Humans and agents communicate through spoken dialogue An agent speaks
Steve’s cognitive architecture
Steve’s cognitive architecture
Separation between domain independent capabilities and domain specific
Separation between domain independent capabilities and domain specific
Course author specifies the domain knowledge in a declarative language
Course author specifies the domain knowledge in a declarative language
Steve – acts as a tutor or learning companion Steve was extended to
Steve – acts as a tutor or learning companion Steve was extended to
Tasks were extended with roles for different participants Planning is
Tasks were extended with roles for different participants Planning is
Learning Companion that recognizes affect
Learning Companion that recognizes affect
41
41
Agents for LMSs
Agents for LMSs
System architecture 5 components: knowbots, the learner, the knowledge
System architecture 5 components: knowbots, the learner, the knowledge
3 types of knowbots : scheduled - sends a reminder and a report to
3 types of knowbots : scheduled - sends a reminder and a report to
Knowbot structure: user-interface agents checker agents (agents that
Knowbot structure: user-interface agents checker agents (agents that
Email agents are responsible for generating, composing, organizing,
Email agents are responsible for generating, composing, organizing,
Checker agents are responsible for checking assignments for the
Checker agents are responsible for checking assignments for the
Knowbots in the system Posting knowbot - looks for two types of
Knowbots in the system Posting knowbot - looks for two types of
Topic knowbot - is invoked by the student and determines if at least
Topic knowbot - is invoked by the student and determines if at least
4. Agents for e-commerce
4. Agents for e-commerce
Consumer's buying behavior
Consumer's buying behavior
Agents as mediators in eCommerce
Agents as mediators in eCommerce
(a) Comparison shopping agents
(a) Comparison shopping agents
Product brokering let the users create preference profiles allows
Product brokering let the users create preference profiles allows
Merchant brokering finds specifications and product reviews makes
Merchant brokering finds specifications and product reviews makes
(b) Auction bots Agents that can organize and/or participate in online
(b) Auction bots Agents that can organize and/or participate in online
Selling agent parameters set by the user: - desired date to sell the
Selling agent parameters set by the user: - desired date to sell the
Integrates product brokering, merchant brokering, and negotiation User
Integrates product brokering, merchant brokering, and negotiation User
IntelliShoper (U
IntelliShoper (U
Sequence of shopping assistance activities
Sequence of shopping assistance activities
Privacy Agent lets the user take a shopping persona hides identity
Privacy Agent lets the user take a shopping persona hides identity
Interface with the vendors Web sites submitting queries parsing
Interface with the vendors Web sites submitting queries parsing
Temperature update T(t+1) = (1 -
Temperature update T(t+1) = (1 -
References M. Wooldrige
References M. Wooldrige
References - continued S. Case, N. Azarmi, M. Thint, T. Ohtami
References - continued S. Case, N. Azarmi, M. Thint, T. Ohtami
References - continued STEVE: http://www
References - continued STEVE: http://www

Презентация на тему: «Intelligent agents on the Web». Автор: User. Файл: «Intelligent agents on the Web.ppt». Размер zip-архива: 738 КБ.

Intelligent agents on the Web

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1 Intelligent agents on the Web

Intelligent agents on the Web

Adina Magda Florea http://turing.cs.pub.ro/~adina adina@cs.pub.ro

5th International Workshop on Symbolic and Numeric Algorithms for Scientific Computing Timisoara, Romania, October 1-4, 2003

2 IDC (http://www

IDC (http://www

idc.com)

IDC estimates that the global market for software agents grew from $7.2 millions in 1997 to $51.5 millions in 1999 and that it will reach $873.2 millions in 2004, with a compound annual growth rate of 76.2% between 1999 and 2004.

2

3 1. Basic notions of agents and MAS

1. Basic notions of agents and MAS

Agents

Much discussion of what (software) agents are and how they differ from programs in general Much discussion about the difference between software agents and intelligent agents Do they bring us anything new in modelling and constructing our applications?

A.M. Florea, SYNASC’03

3

4 Agents characteristics

Agents characteristics

act on behalf of a user or a / another program operate without the direct intervention of humans and have control over their actions and internal state - autonomy sense the environment and acts upon it - reactivity capable purposeful action - pro-activity goal-directed vs reactive behaviour? interact with other agents and humans - social ability function continuously - persistent software mobility ?

A.M. Florea, SYNASC’03

4

5 Motivations for agents

Motivations for agents

Large-scale, complex, distributed systems: understand, built, manage Open and heterogeneous systems - build components independently Distribution of resources Distribution of expertise Needs for personalization and customization Interoperability of pre-existing systems / integration of legacy systems

A.M. Florea, SYNASC’03

5

6 Agents vs Objects

Agents vs Objects

Autonomy - stronger - agents have sole control over their actions, an agent may refuse or ask for compensation Flexibility - Agents are reactive, like objects, but also pro-active Higher level communication than object messages Agents are usually persistent Own thread of control

A.M. Florea, SYNASC’03

6

7 Multi-agent systems

Multi-agent systems

Many entities (agents) in a common environment

Environment

Influenece area

Interactions

A.M. Florea, SYNASC’03

7

8 Multi-agent systems

Multi-agent systems

High-level interactions Interactions for - coordination - communication - organization Coordination ? collectively motivated / interested ? self interested - own goals / indifferent - own goals / competition / competing for the same resources - own goals / competition / contradictory goals - own goals / coalitions

8

9 Multi-agent systems

Multi-agent systems

Communication ? communication protocol ? communication language - negotiation to reach agreement - ontology Organizational structures ? centralized vs decentralized ? hierarchical/ markets

9

10 Cognitive agents

Cognitive agents

The model of human intelligence and human perspective of the world ? characterise an intelligent agent using symbolic representations and mentalistic notions: knowledge - John knows humans are mortal beliefs - John took his umbrella because he believed it was going to rain desires, goals - John wants to possess a PhD intentions - John intends to work hard in order to have a PhD choices - John decided to apply for a PhD commitments - John will not stop working until getting his PhD obligations - John has to work to make a living (Shoham, 1993)

10

11 Premises of cognitive agents

Premises of cognitive agents

Such a mentalistic or intentional view of agents - a kind of "folk psychology" – it is a useful paradigm for describing complex distributed systems. The complexity of a system or the fact that we can not know or predict the internal structure of all components seems to imply that we must rely on animistic, intentional explanation of system functioning and behavior.

11

12 Reactive agents

Reactive agents

Simple processing units that perceive and react to changes in their environment. Do not have a symbolic representation of the world and do not use complex symbolic reasoning. Intelligence is not a property of the active entity but it is distributed in the system, and steams as the result of the interaction between the many entities of the distributed structure and the environment.

12

13 2. Information agents

2. Information agents

Several types of information agents

Personal agents provide "intelligent" and user-friendly interfaces observe the user and learn user’s 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

13

14 Cooperative information retrieval systems

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

14

15 RETSINA: Reusable Environment for Task-Structured Intelligent

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

15

16 RETSINA MAS architecture

RETSINA MAS architecture

16

17 The agent architecture

The agent architecture

17

18 WebMate – an information search agent in RETSINA

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

18

19 Trigger Pair Model If a word S is significantly correlated with

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

19

A.M. Florea, Feb 2003

20 Information agents for e-communities (BTexact Technologies)

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

20

21 Profiler Agent one for each user stores interest information in a

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

21

22 Application agents Bugle – uses profile information to generate a

Application agents Bugle – uses profile information to generate a

daily newsletter that contains articles relevant to the user’s 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 user’s 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.

22

23 3. Agents for e-learning

3. Agents for e-learning

Agent’s 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 instructor’s activities

23

24 ADELE

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.

24

25 Adele consists of a pedagogical agent and a 2D animated persona, which

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 student’s 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.

25

26 Simulations created for the course in diagnostic skill development

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.

26

27 Trauma care is a collaborative activity - physicians and paramedics

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.

27

28 ADELE’s architecture

ADELE’s 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

28

29 Task representation Task plan = task steps and their dependencies,

Task representation Task plan = task steps and their dependencies,

step rationale task steps = object-oriented data structures processed by Adele’s 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 Adele’s reasoning ? situation-monitoring task; ? situation-based reasoning.

29

30 Pedagogy Situation-based reasoning allows the recognition of

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 student’s actions analyze student’s record and provides domain appropriate feedback (e.g., evaluation of diagnosis, evaluation of diagnostic’s costs, evaluation of the steps taken).

30

31 Adele’s persona Uses gaze and gestures to react to student’s actions –

Adele’s persona Uses gaze and gestures to react to student’s actions –

repertoire of facial expressions and body postures that represent emotions: surprise, disappointment, etc. Senses user’s 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 => makes possible to run on a variety of desktops (no 3D graphics needed).

31

32 STEVE

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

32

33 Students and Steve agents are immersed in the simulation environment

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 Martin’s 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

33

34 Humans and agents communicate through spoken dialogue An agent speaks

Humans and agents communicate through spoken dialogue An agent speaks

to a person by sending a message to the person’s text-to-speech software – broadcasts the utterance through the headphones mounted on the HMD Entropic’s 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. Entropic’s GrapHvite for speech recognition

34

35 Steve’s cognitive architecture

Steve’s 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

35

36 Separation between domain independent capabilities and domain specific

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 student’s actions question answering (What should I do next?, Why?) episodic memory communication control of human figure

36

37 Course author specifies the domain knowledge in a declarative language

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

37

38 Steve – acts as a tutor or learning companion Steve was extended to

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

38

39 Tasks were extended with roles for different participants Planning is

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

39

40 Learning Companion that recognizes affect

Learning Companion that recognizes affect

Affect recognition

MIT Media Lab Affective states significant to learning: anxiety, worry/boredom, indifference, interest, curiosity, confident, etc. on-goal and off-goal

Posture Eye-gaze Facial expression Hand movement

40

41 41

41

42 Agents for LMSs

Agents for LMSs

Knowbots (or Knowledge Robots) created to automate the repetitive tasks of human facilitators in online workshops A system developed at ALN Center at Vanderbilt University, Nashville, TN

42

43 System architecture 5 components: knowbots, the learner, the knowledge

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.

43

44 3 types of knowbots : scheduled - sends a reminder and a report to

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.

44

45 Knowbot structure: user-interface agents checker agents (agents that

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.

45

46 Email agents are responsible for generating, composing, organizing,

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.

46

47 Checker agents are responsible for checking assignments for the

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.

47

48 Knowbots in the system Posting knowbot - looks for two types of

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

48

49 Topic knowbot - is invoked by the student and determines if at least

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

49

50 4. Agents for e-commerce

4. Agents for e-commerce

Electronic commerce

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

50

51 Consumer's buying behavior

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

51

52 Agents as mediators in eCommerce

Agents as mediators in eCommerce

Persona Bargain Logic Firefly Finder Jango Kasbah T@T IntelliShoper Need identification Product brokering Merchant brokering Negotiation Purchase and delivery Product service

52

53 (a) Comparison shopping agents

(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 user’s profiles constraint-based filtering – specifying constraints (price, date limit)

53

54 Product brokering let the users create preference profiles allows

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

54

55 Merchant brokering finds specifications and product reviews makes

Merchant brokering finds specifications and product reviews makes

recommendations to the user submit queries to vendor’s 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 product’s features Web pages are different; exploits: ? Navigation regularities (easy to find products) ? Corporate regularities (similar look’n’feel) ? Vertical separation (use of white spaces) 1999 ? Excite

55

56 (b) Auction bots Agents that can organize and/or participate in online

(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

56

57 Selling agent parameters set by the user: - desired date to sell the

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

57

58 Integrates product brokering, merchant brokering, and negotiation User

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)

58

59 IntelliShoper (U

IntelliShoper (U

Iowa)

Integrates product brokering, merchant brokering, and negotiation Goals: Customize behavior adaptively by learning user’s preferences Provides assistance by remaining autonomous from both customer and vendors Protect shoppers’ privacy by concealing their identities and behavior from vendors

59

60 Sequence of shopping assistance activities

Sequence of shopping assistance activities

Privacy Agent

Monitor Agent

Learning Agent

Vendor plug-ins

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 user’s 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

Anonymizing server

IntelliShoper server

60

61 Privacy Agent lets the user take a shopping persona hides identity

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

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

61

62 Interface with the vendors Web sites submitting queries parsing

Interface with the vendors Web sites submitting queries parsing

results Language for specifying vendor dependent logic – based on XML and inspired by Apple’s Sherlock engine Persona’s 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

62

63 Temperature update T(t+1) = (1 -

Temperature update T(t+1) = (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.

63

64 References M. Wooldrige

References M. Wooldrige

An Introduction to MultiAgent Systems, John Wiley&Sons, 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.

64

65 References - continued S. Case, N. Azarmi, M. Thint, T. Ohtami

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

65

66 References - continued STEVE: http://www

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 11:47-78, 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.

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