Intelligent agents on the WebAdina Magda Florea

http://turing.cs.pub.ro/~adina

adina@cs.pub.ro

5th International Workshopon Symbolic and Numeric Algorithms for Scientific Computing

Timisoara, Romania, October 1-4, 2003

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.

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AgentsAgents 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?

1. Basic notions of agents 1. Basic notions of agents and MASand MAS

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Agents characteristicsAgents 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 ?

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Motivations for agentsMotivations 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

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Agents vs ObjectsAgents 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

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Multi-agent systemsMulti-agent systems

Many entities (agents) in a common environment

Environment

Influenece area Interactions

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Multi-agent systemsMulti-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

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Multi-agent systemsMulti-agent systems

Communication communication protocol communication language

- negotiation to reach agreement- ontology

Organizational structures centralized vs decentralized hierarchical/ markets

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Cognitive agentsCognitive 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)

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Premises of cognitive agentsPremises 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.

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Reactive agentsReactive 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.

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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

2. Information agents2. Information agents

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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

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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

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RETSINA MAS architectureRETSINA MAS architecture

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The agent architectureThe agent architecture

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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

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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

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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

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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

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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.

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Agent’s roles in e-learningAgent’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

3. Agents for e-learning3. Agents for e-learning

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ADELEADELE

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.

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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.

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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.

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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.

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ADELE’s architectureADELE’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 stateStudent record of actions

Simulation engine and GUI

Animated persona Reasoning engine

Text-to-speech engine

Adele client

Adele server

Task Planner, Assessor

Store for case, student, persona, references, and simulation parameters

Webserver

Webbrowser

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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.

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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).

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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).

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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

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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

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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

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Steve’s cognitive architecture

Task knowledge

Pedagogicalcapabilities

Soar rules

MotorControl

Perception

Perception snapshotsimportant events

Abstract motorcommands

Spatial properties

Detailed motorcommands

Relevantevents

MessageDispatcher

SimulatorInterfacecomponentsVisual, audio

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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

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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

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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

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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

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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

Affect recognition

Posture Eye-gaze Facial expression Hand movement

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  On Task Off Task

Posture Leaning forward Slumping on the chair

Eye-gaze Looking towards the problem

Looking everywhere else

Facial expression

Eyes tighteningEyes wideningRaising eyebrowsSmile

Lowering eyebrowNose wrinklingDepressing lower lip corner

Hand movement

Typing, clicking mouse

Hands not on the mouse/keyboard

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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

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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.

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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.

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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.

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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.

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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.

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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.

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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

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Electronic commerceElectronic commerce Transactions - business-to-busines (B2B)

- business-to-consumer (B2C)

- consumer-to-consumer (C2C)

Difficulties of eCommerceDifficulties of eCommerce Trust

Privacy and security

Billing

Reliability

4. Agents for e-commerce4. Agents for e-commerce

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Consumer's buying behaviorConsumer'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

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Agents as mediators in eCommerceAgents as mediators in eCommerce

Persona Bargain

Logic Firefly Finder Jango Kasbah T@T IntelliShoper

Need

identification

Productbrokering

Merchantbrokering

Negotiation

Purchaseand delivery

Productservice

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(a) Comparison shopping agents

Search online shops to find products, merchants and best deals

Product brokeringTechniques: feature-based filtering – feature keywords collaborative filtering – similarities between user’s

profiles constraint-based filtering – specifying constraints

(price, date limit)

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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

Persona Logic

Firefly

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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

Jango

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(b) Auction botsAgents 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

Kasbah

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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 time57A.M. Florea, SYNASC’03

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)

Tête-à-tête

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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

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PrivacyAgent

ShoppingPersona

Anonymizingserver

IntelliShoper server

LearningAgent

MonitorAgent

Vendorplug-ins

MySQL

VendorWeb sites

Sequence of shopping assistance activities

1. The user creates an account and one or more personae2. The user takes on a persona3. The persona initiates a shopping session by submitting a query to the LA4. The LA stores the user’s request in the database5. The LA uses vendors plug-ins to send requests to vendors6. Results from vendors are parsed through the vendors plug-ins7. IS stores the result in the database8. The LA uses the persona profile to rank the hits9. The LA presents the results to the persona10. The PA forwards the results to the user11. The user can further interact with the LA

Basicinteractionloop

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12. The MA loads standing queries from the database13. The MA uses vendor plug-ins to check for any new results from the vendors14. IS parses new and updated hits15. IS stores the hits in the database until the users logs in again

Occursoffline

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

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Interface with the vendors Web sites submitting queriesparsing results

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

Persona’s ProfilePreferenceKeywordsRelevant features: numeric (discretized) and textual

(keywords) Updating the user profile

Temperatures for featuresUpdates temperatures after any user action related to a

given hit

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Temperature updateT(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.

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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.

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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

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References - continuedSTEVE: 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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